Project: FBI Gun Data Analysis¶

By: Ambar Canonicco

Table of Contents¶

Introduction
Data Wrangling
Exploratory Data Analysis
Conclusions

Introduction¶

In this project I am going to use Python programatic language to wrangle and explore given data of census and FBI gun data registered in the US. With the overview of what the data should look like, I will focus my analysis to answer this question:

What census data is most associated with high gun per capita?

# Import necessary modules
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display as dpl
import time
import seaborn as sb
start_time = time.time()
%matplotlib inline

Data Wrangling¶

Data Gathering¶

The data was already provided to me. However in this step I will prepare my notebook and store the data so I can work with it through the project.

# Store file names
file1 = './data/gun_data.xlsx'
file2 = './data/U.S. Census Data.csv'

# Store data files
gun_data_raw = pd.read_excel(file1)
census_data_raw = pd.read_csv(file2)
data_sets = [gun_data_raw,census_data_raw]

#Preview Dataframes
for i in data_sets:
    dpl(i.head(2))
    dpl(i.tail(2))

I just had a very narrow idea of what the data looks like. Either way it helps to have an overview.

Data Assessment¶

#Assess gun data types
gun_data_raw.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12485 entries, 0 to 12484
Data columns (total 27 columns):
xlsx                         12485 non-null object
state                        12485 non-null object
permit                       12461 non-null float64
permit_recheck               1100 non-null float64
handgun                      12465 non-null float64
long_gun                     12466 non-null float64
other                        5500 non-null float64
multiple                     12485 non-null int64
admin                        12462 non-null float64
prepawn_handgun              10542 non-null float64
prepawn_long_gun             10540 non-null float64
prepawn_other                5115 non-null float64
redemption_handgun           10545 non-null float64
redemption_long_gun          10544 non-null float64
redemption_other             5115 non-null float64
returned_handgun             2200 non-null float64
returned_long_gun            2145 non-null float64
returned_other               1815 non-null float64
rentals_handgun              990 non-null float64
rentals_long_gun             825 non-null float64
private_sale_handgun         2750 non-null float64
private_sale_long_gun        2750 non-null float64
private_sale_other           2750 non-null float64
return_to_seller_handgun     2475 non-null float64
return_to_seller_long_gun    2750 non-null float64
return_to_seller_other       2255 non-null float64
totals                       12485 non-null int64
dtypes: float64(23), int64(2), object(2)
memory usage: 2.6+ MB

#Assess census data types
census_data_raw.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 85 entries, 0 to 84
Data columns (total 52 columns):
Fact              80 non-null object
Fact Note         28 non-null object
Alabama           65 non-null object
Alaska            65 non-null object
Arizona           65 non-null object
Arkansas          65 non-null object
California        65 non-null object
Colorado          65 non-null object
Connecticut       65 non-null object
Delaware          65 non-null object
Florida           65 non-null object
Georgia           65 non-null object
Hawaii            65 non-null object
Idaho             65 non-null object
Illinois          65 non-null object
Indiana           65 non-null object
Iowa              65 non-null object
Kansas            65 non-null object
Kentucky          65 non-null object
Louisiana         65 non-null object
Maine             65 non-null object
Maryland          65 non-null object
Massachusetts     65 non-null object
Michigan          65 non-null object
Minnesota         65 non-null object
Mississippi       65 non-null object
Missouri          65 non-null object
Montana           65 non-null object
Nebraska          65 non-null object
Nevada            65 non-null object
New Hampshire     65 non-null object
New Jersey        65 non-null object
New Mexico        65 non-null object
New York          65 non-null object
North Carolina    65 non-null object
North Dakota      65 non-null object
Ohio              65 non-null object
Oklahoma          65 non-null object
Oregon            65 non-null object
Pennsylvania      65 non-null object
Rhode Island      65 non-null object
South Carolina    65 non-null object
South Dakota      65 non-null object
Tennessee         65 non-null object
Texas             65 non-null object
Utah              65 non-null object
Vermont           65 non-null object
Virginia          65 non-null object
Washington        65 non-null object
West Virginia     65 non-null object
Wisconsin         65 non-null object
Wyoming           65 non-null object
dtypes: object(52)
memory usage: 34.6+ KB

# Visually assess gun data
gun_data_raw

# Visually assess census data
census_data_raw

# Have an overview of how Fact series looks like
census_data_raw.Fact.value_counts()

High school graduate or higher, percent of persons age 25 years+, 2011-2015               1
With a disability, under age 65 years, percent, 2011-2015                                 1
(a)                                                                                       1
White alone, not Hispanic or Latino, percent, July 1, 2016,  (V2016)                      1
Median gross rent, 2011-2015                                                              1
S                                                                                         1
In civilian labor force, total, percent of population age 16 years+, 2011-2015            1
Minority-owned firms, 2012                                                                1
Veteran-owned firms, 2012                                                                 1
Hispanic or Latino, percent, July 1, 2016,  (V2016)                                       1
Mean travel time to work (minutes), workers age 16 years+, 2011-2015                      1
Persons under 18 years, percent, April 1, 2010                                            1
Persons  without health insurance, under age 65 years, percent                            1
All firms, 2012                                                                           1
Total employment, 2015                                                                    1
Black or African American alone, percent, July 1, 2016,  (V2016)                          1
Living in same house 1 year ago, percent of persons age 1 year+, 2011-2015                1
Men-owned firms, 2012                                                                     1
Households, 2011-2015                                                                     1
Foreign born persons, percent, 2011-2015                                                  1
Female persons, percent,  July 1, 2016,  (V2016)                                          1
D                                                                                         1
Total employer establishments, 2015                                                       1
Bachelor's degree or higher, percent of persons age 25 years+, 2011-2015                  1
Value Notes                                                                               1
Total retail sales, 2012 ($1,000)                                                         1
Persons under 5 years, percent, July 1, 2016,  (V2016)                                    1
Value Flags                                                                               1
FN                                                                                        1
Two or More Races, percent, July 1, 2016,  (V2016)                                        1
-                                                                                         1
FIPS Code                                                                                 1
Population estimates base, April 1, 2010,  (V2016)                                        1
Housing units,  July 1, 2016,  (V2016)                                                    1
F                                                                                         1
American Indian and Alaska Native alone, percent, July 1, 2016,  (V2016)                  1
Per capita income in past 12 months (in 2015 dollars), 2011-2015                          1
X                                                                                         1
Nonveteran-owned firms, 2012                                                              1
Language other than English spoken at home, percent of persons age 5 years+, 2011-2015    1
(b)                                                                                       1
Total retail sales per capita, 2012                                                       1
In civilian labor force, female, percent of population age 16 years+, 2011-2015           1
Total accommodation and food services sales, 2012 ($1,000)                                1
Total annual payroll, 2015 ($1,000)                                                       1
Median selected monthly owner costs -with a mortgage, 2011-2015                           1
Population, Census, April 1, 2010                                                         1
NOTE: FIPS Code values are enclosed in quotes to ensure leading zeros remain intact.      1
Women-owned firms, 2012                                                                   1
Total nonemployer establishments, 2015                                                    1
Total merchant wholesaler sales, 2012 ($1,000)                                            1
Total manufacturers shipments, 2012 ($1,000)                                              1
Persons under 18 years, percent, July 1, 2016,  (V2016)                                   1
Total health care and social assistance receipts/revenue, 2012 ($1,000)                   1
Z                                                                                         1
Veterans, 2011-2015                                                                       1
Population estimates, July 1, 2016,  (V2016)                                              1
(c)                                                                                       1
Median household income (in 2015 dollars), 2011-2015                                      1
Population per square mile, 2010                                                          1
Median value of owner-occupied housing units, 2011-2015                                   1
1                                                                                         1
Building permits, 2016                                                                    1
Persons under 5 years, percent, April 1, 2010                                             1
White alone, percent, July 1, 2016,  (V2016)                                              1
Housing units, April 1, 2010                                                              1
Persons 65 years and over, percent, April 1, 2010                                         1
Female persons, percent, April 1, 2010                                                    1
Nonminority-owned firms, 2012                                                             1
Median selected monthly owner costs -without a mortgage, 2011-2015                        1
Fact Notes                                                                                1
Population, percent change - April 1, 2010 (estimates base) to July 1, 2016,  (V2016)     1
Asian alone, percent, July 1, 2016,  (V2016)                                              1
Persons 65 years and over, percent,  July 1, 2016,  (V2016)                               1
Owner-occupied housing unit rate, 2011-2015                                               1
Total employment, percent change, 2014-2015                                               1
Land area in square miles, 2010                                                           1
Persons per household, 2011-2015                                                          1
Persons in poverty, percent                                                               1
Native Hawaiian and Other Pacific Islander alone, percent, July 1, 2016,  (V2016)         1
Name: Fact, dtype: int64

Notes¶

During visual assessment I noted some issues better explained below.

Gun Data:¶

Series xslx should be renamed and splitted into year and month
Recalculate totals to check if NaNs are missing data or zeros

Census Data:¶

Series fact and fact note should be renamed and splitted
Numbers are stored as strings
Table should be transposed
All null series, after transposed, should be dropped
Fact series contains category and date
Fact note contains only(?) invalid values

Data Cleaning¶

I've noted in the previous step some issues in each dataframe before I can start making some analysis. Therefore I will clean the data to fix thouse issues noted above.

Gun Data:¶

Split xlsx
Rename those columns as 'Year' and 'Month'
Change dtypes from floats to integers
Recalculate totals and compared them with already populated totals
Deal with NaNs based on result of recalculation

Census_Data:¶

Split Fact into 'Census', 'Year' and 'Month'
Transpose table
Redefine datatypes as corresponding numbers (drop percent symbols)
Drop all NaNs series
Drop Fact note (?)

Gun Data Cleaning¶

# Split xlsx and rename those columns as 'year' and 'month'
new = gun_data_raw['xlsx'].str.split("-", n = 1, expand = True)

# Rename those columns as 'year' and 'month'
clean_gun_data = gun_data_raw
# making separate year column from new data frame 
clean_gun_data['year']= new[0] 
  
# making separate month column from new data frame 
clean_gun_data['month']= new[1] 

cols = gun_data_raw.columns.tolist()
cols = cols[-2:] + cols [1:-2]

clean_gun_data = clean_gun_data[cols]

clean_gun_data.sample(10)

clean_gun_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12485 entries, 0 to 12484
Data columns (total 28 columns):
year                         12485 non-null object
month                        12485 non-null object
state                        12485 non-null object
permit                       12461 non-null float64
permit_recheck               1100 non-null float64
handgun                      12465 non-null float64
long_gun                     12466 non-null float64
other                        5500 non-null float64
multiple                     12485 non-null int64
admin                        12462 non-null float64
prepawn_handgun              10542 non-null float64
prepawn_long_gun             10540 non-null float64
prepawn_other                5115 non-null float64
redemption_handgun           10545 non-null float64
redemption_long_gun          10544 non-null float64
redemption_other             5115 non-null float64
returned_handgun             2200 non-null float64
returned_long_gun            2145 non-null float64
returned_other               1815 non-null float64
rentals_handgun              990 non-null float64
rentals_long_gun             825 non-null float64
private_sale_handgun         2750 non-null float64
private_sale_long_gun        2750 non-null float64
private_sale_other           2750 non-null float64
return_to_seller_handgun     2475 non-null float64
return_to_seller_long_gun    2750 non-null float64
return_to_seller_other       2255 non-null float64
totals                       12485 non-null int64
dtypes: float64(23), int64(2), object(3)
memory usage: 2.7+ MB

# Recalculate totals and compared them with already populated totals
# Define df to sum
dfcols = clean_gun_data.columns[3:-1]
df = clean_gun_data[dfcols]
clean_gun_data['recalculation'] = df.sum(axis=1)
# Deal with NaNs
clean_gun_data = clean_gun_data.fillna(0, inplace = False)
clean_gun_data.sample(10)

# Change dtypes from floats to integers
cols2 = list(clean_gun_data.columns)
cols_dict = {}
for i in cols2[0:3]:
    cols_dict.update({i: object})
for i in cols2[3:]:
    cols_dict.update({i: int})
cols_dict

{'year': object,
 'month': object,
 'state': object,
 'permit': int,
 'permit_recheck': int,
 'handgun': int,
 'long_gun': int,
 'other': int,
 'multiple': int,
 'admin': int,
 'prepawn_handgun': int,
 'prepawn_long_gun': int,
 'prepawn_other': int,
 'redemption_handgun': int,
 'redemption_long_gun': int,
 'redemption_other': int,
 'returned_handgun': int,
 'returned_long_gun': int,
 'returned_other': int,
 'rentals_handgun': int,
 'rentals_long_gun': int,
 'private_sale_handgun': int,
 'private_sale_long_gun': int,
 'private_sale_other': int,
 'return_to_seller_handgun': int,
 'return_to_seller_long_gun': int,
 'return_to_seller_other': int,
 'totals': int,
 'recalculation': int}

# Change dtypes from floats to integers (cont.)
clean_gun_data = clean_gun_data.astype(cols_dict)

clean_gun_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12485 entries, 0 to 12484
Data columns (total 29 columns):
year                         12485 non-null object
month                        12485 non-null object
state                        12485 non-null object
permit                       12485 non-null int64
permit_recheck               12485 non-null int64
handgun                      12485 non-null int64
long_gun                     12485 non-null int64
other                        12485 non-null int64
multiple                     12485 non-null int64
admin                        12485 non-null int64
prepawn_handgun              12485 non-null int64
prepawn_long_gun             12485 non-null int64
prepawn_other                12485 non-null int64
redemption_handgun           12485 non-null int64
redemption_long_gun          12485 non-null int64
redemption_other             12485 non-null int64
returned_handgun             12485 non-null int64
returned_long_gun            12485 non-null int64
returned_other               12485 non-null int64
rentals_handgun              12485 non-null int64
rentals_long_gun             12485 non-null int64
private_sale_handgun         12485 non-null int64
private_sale_long_gun        12485 non-null int64
private_sale_other           12485 non-null int64
return_to_seller_handgun     12485 non-null int64
return_to_seller_long_gun    12485 non-null int64
return_to_seller_other       12485 non-null int64
totals                       12485 non-null int64
recalculation                12485 non-null int64
dtypes: int64(26), object(3)
memory usage: 2.8+ MB

# Recalculate totals and compared them with already populated totals (cont.)
clean_gun_data['comparison'] = (clean_gun_data['totals'] == clean_gun_data['recalculation'])

clean_gun_data.comparison.value_counts()

True    12485
Name: comparison, dtype: int64

Confirmed that NaNs can be treated as zeros.

# Set states as index
clean_gun_data_cols = clean_gun_data.columns[0:-2]
clean_gun_data = clean_gun_data[clean_gun_data_cols]
clean_gun_data = clean_gun_data.set_index('state')
clean_gun_data.sample(10)

# Filter by July
clean_gun_data = clean_gun_data[clean_gun_data.month.isin(['07'])]
clean_gun_data

# Obtaining 2016 data
clean_gun_2016 = clean_gun_data[clean_gun_data.year.str.contains('2016')]
clean_gun_2016.sample(10)

Census Data Cleaning¶

# Limit DataFrame to 2016 data
clean_census_data = census_data_raw[census_data_raw.Fact.notnull()][census_data_raw.Fact.dropna().str.contains('2016')]
clean_census_data.sample(10)

# Set Fact as index and Transpose table
clean_census_data = clean_census_data.set_index('Fact').transpose()
clean_census_data.head(10)

# Drop 'Fact Note', which is NaN
clean_census_data = clean_census_data[1:]
clean_census_data.sample(10)

# Assess datatypes
clean_census_data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 50 entries, Alabama to Wyoming
Data columns (total 17 columns):
Population estimates, July 1, 2016,  (V2016)                                             50 non-null object
Population estimates base, April 1, 2010,  (V2016)                                       50 non-null object
Population, percent change - April 1, 2010 (estimates base) to July 1, 2016,  (V2016)    50 non-null object
Persons under 5 years, percent, July 1, 2016,  (V2016)                                   50 non-null object
Persons under 18 years, percent, July 1, 2016,  (V2016)                                  50 non-null object
Persons 65 years and over, percent,  July 1, 2016,  (V2016)                              50 non-null object
Female persons, percent,  July 1, 2016,  (V2016)                                         50 non-null object
White alone, percent, July 1, 2016,  (V2016)                                             50 non-null object
Black or African American alone, percent, July 1, 2016,  (V2016)                         50 non-null object
American Indian and Alaska Native alone, percent, July 1, 2016,  (V2016)                 50 non-null object
Asian alone, percent, July 1, 2016,  (V2016)                                             50 non-null object
Native Hawaiian and Other Pacific Islander alone, percent, July 1, 2016,  (V2016)        50 non-null object
Two or More Races, percent, July 1, 2016,  (V2016)                                       50 non-null object
Hispanic or Latino, percent, July 1, 2016,  (V2016)                                      50 non-null object
White alone, not Hispanic or Latino, percent, July 1, 2016,  (V2016)                     50 non-null object
Housing units,  July 1, 2016,  (V2016)                                                   50 non-null object
Building permits, 2016                                                                   50 non-null object
dtypes: object(17)
memory usage: 7.0+ KB

# Drop Building permits, 2016, which is not useful
clean_census_data = clean_census_data[list(clean_census_data.columns)[0:-1]]
list(clean_census_data.columns)

['Population estimates, July 1, 2016,  (V2016)',
 'Population estimates base, April 1, 2010,  (V2016)',
 'Population, percent change - April 1, 2010 (estimates base) to July 1, 2016,  (V2016)',
 'Persons under 5 years, percent, July 1, 2016,  (V2016)',
 'Persons under 18 years, percent, July 1, 2016,  (V2016)',
 'Persons 65 years and over, percent,  July 1, 2016,  (V2016)',
 'Female persons, percent,  July 1, 2016,  (V2016)',
 'White alone, percent, July 1, 2016,  (V2016)',
 'Black or African American alone, percent, July 1, 2016,  (V2016)',
 'American Indian and Alaska Native alone, percent, July 1, 2016,  (V2016)',
 'Asian alone, percent, July 1, 2016,  (V2016)',
 'Native Hawaiian and Other Pacific Islander alone, percent, July 1, 2016,  (V2016)',
 'Two or More Races, percent, July 1, 2016,  (V2016)',
 'Hispanic or Latino, percent, July 1, 2016,  (V2016)',
 'White alone, not Hispanic or Latino, percent, July 1, 2016,  (V2016)',
 'Housing units,  July 1, 2016,  (V2016)']

# Redefine datatypes as corresponding numbers (and drop percent symbols)
for i in list(clean_census_data.columns):
    if 'percent' in i:
        clean_census_data_v2[i] = clean_census_data[i].str.replace('%','')
    else:
        clean_census_data_v2[i] = clean_census_data[i].str.replace(',','')
clean_census_data_v2.sample(10)

Noted some 'Z' in Native Hawaiian and Other Pacific Islander alone, percent, July 1, 2016, (V2016). I decided to replace them with zeros.

# Get exact column name
list(clean_census_data_v2.columns)

['Population estimates, July 1, 2016,  (V2016)',
 'Population estimates base, April 1, 2010,  (V2016)',
 'Population, percent change - April 1, 2010 (estimates base) to July 1, 2016,  (V2016)',
 'Persons under 5 years, percent, July 1, 2016,  (V2016)',
 'Persons under 18 years, percent, July 1, 2016,  (V2016)',
 'Persons 65 years and over, percent,  July 1, 2016,  (V2016)',
 'Female persons, percent,  July 1, 2016,  (V2016)',
 'White alone, percent, July 1, 2016,  (V2016)',
 'Black or African American alone, percent, July 1, 2016,  (V2016)',
 'American Indian and Alaska Native alone, percent, July 1, 2016,  (V2016)',
 'Asian alone, percent, July 1, 2016,  (V2016)',
 'Native Hawaiian and Other Pacific Islander alone, percent, July 1, 2016,  (V2016)',
 'Two or More Races, percent, July 1, 2016,  (V2016)',
 'Hispanic or Latino, percent, July 1, 2016,  (V2016)',
 'White alone, not Hispanic or Latino, percent, July 1, 2016,  (V2016)',
 'Housing units,  July 1, 2016,  (V2016)']

# Clean Native Hawaiian and Other Pacific Islander alone, percent, July 1, 2016, (V2016)
clean_census_data_v2['Native Hawaiian and Other Pacific Islander alone, percent, July 1, 2016,  (V2016)'] = clean_census_data_v2['Native Hawaiian and Other Pacific Islander alone, percent, July 1, 2016,  (V2016)'].str.replace('Z','0')
clean_census_data_v2

# Redefine datatypes as corresponding numbers (and drop percent symbols) (cont.)
cols4 = list(clean_census_data_v2.columns)
cols_dict2 = {}
for i in cols4[0:1]:
    cols_dict2.update({i: int})
for i in cols4[:-1]:
    cols_dict2.update({i: int})
for i in cols4[2:-1]:
    cols_dict2.update({i: float})
for i in cols4[-1:]:
    cols_dict2.update({i: int})
    
clean_census_data_v2 = clean_census_data_v2.astype(cols_dict2)

clean_census_data_v2.info()

<class 'pandas.core.frame.DataFrame'>
Index: 50 entries, Alabama to Wyoming
Data columns (total 16 columns):
Population estimates, July 1, 2016,  (V2016)                                             50 non-null int64
Population estimates base, April 1, 2010,  (V2016)                                       50 non-null int64
Population, percent change - April 1, 2010 (estimates base) to July 1, 2016,  (V2016)    50 non-null float64
Persons under 5 years, percent, July 1, 2016,  (V2016)                                   50 non-null float64
Persons under 18 years, percent, July 1, 2016,  (V2016)                                  50 non-null float64
Persons 65 years and over, percent,  July 1, 2016,  (V2016)                              50 non-null float64
Female persons, percent,  July 1, 2016,  (V2016)                                         50 non-null float64
White alone, percent, July 1, 2016,  (V2016)                                             50 non-null float64
Black or African American alone, percent, July 1, 2016,  (V2016)                         50 non-null float64
American Indian and Alaska Native alone, percent, July 1, 2016,  (V2016)                 50 non-null float64
Asian alone, percent, July 1, 2016,  (V2016)                                             50 non-null float64
Native Hawaiian and Other Pacific Islander alone, percent, July 1, 2016,  (V2016)        50 non-null float64
Two or More Races, percent, July 1, 2016,  (V2016)                                       50 non-null float64
Hispanic or Latino, percent, July 1, 2016,  (V2016)                                      50 non-null float64
White alone, not Hispanic or Latino, percent, July 1, 2016,  (V2016)                     50 non-null float64
Housing units,  July 1, 2016,  (V2016)                                                   50 non-null int64
dtypes: float64(13), int64(3)
memory usage: 9.1+ KB

# Change percentage series into valid numbers
clean_census_data_v2_cols = []
for i in list(clean_census_data_v2.columns):
    if 'percent' in i:
        clean_census_data_v2_cols.append(i)
for i in clean_census_data_v2_cols:
    clean_census_data_v2[i] = clean_census_data_v2[i]/100
clean_census_data_v2.head()

# Get Population, percent change - April 1, 2010 (estimates base) to July 1, 2016, (V2016) alone
Change_census = clean_census_data_v2['Population, percent change - April 1, 2010 (estimates base) to July 1, 2016,  (V2016)']
Change_census

Alabama           0.017000
Alaska            0.045000
Arizona           0.084000
Arkansas          0.025000
California        0.054000
Colorado          0.102000
Connecticut       0.001000
Delaware          0.060000
Florida           0.096000
Georgia           0.064000
Hawaii            0.050000
Idaho             0.074000
Illinois         -0.002000
Indiana           0.023000
Iowa              0.029000
Kansas            0.019000
Kentucky          0.022000
Louisiana         0.033000
Maine             0.002000
Maryland          0.042000
Massachusetts     0.040000
Michigan          0.004000
Minnesota         0.041000
Mississippi       0.007000
Missouri          0.017000
Montana           0.054000
Nebraska          0.044000
Nevada            0.089000
New Hampshire     0.014000
New Jersey        0.017000
New Mexico        0.000110
New York          0.000190
North Carolina    0.000640
North Dakota      0.001270
Ohio              0.000070
Oklahoma          0.000460
Oregon            0.000680
Pennsylvania      0.000060
Rhode Island      0.000030
South Carolina    0.000730
South Dakota      0.000630
Tennessee         0.000480
Texas             0.108000
Utah              0.104000
Vermont          -0.002000
Virginia          0.051000
Washington        0.084000
West Virginia    -0.012000
Wisconsin         0.016000
Wyoming           0.039000
Name: Population, percent change - April 1, 2010 (estimates base) to July 1, 2016,  (V2016), dtype: float64

# Get only July Census
clean_census_data_v3_cols2 = []
for i in list(clean_census_data_v2.columns):
    if 'April' not in i:
        clean_census_data_v3_cols2.append(i)

clean_census_data_v3 = clean_census_data_v2[clean_census_data_v3_cols2]
clean_census_data_v3.head()

#Rename clean data
clean_census_2016 = clean_census_data_v3
clean_census_2016.sample(10)

Exploratory Data Analysis¶

# Rename data for analysis purposes
gun = clean_gun_2016
census = clean_census_2016

# Replace empty spaces in columns names
census.columns = census.columns.str.replace(',','')
census.columns = census.columns.str.replace(' ','_')
census.columns = census.columns.str.replace(',','')
census.columns = census.columns.str.replace(' ','_')

# Define a function to calculate the gun density
def percpcap (x,y):
    # x = gun value
    # y = population
    result = (x/y)
    return result

census

# Join dataframes to perform analysis
df = gun.join(census)
df = df.dropna()

df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 50 entries, Alabama to Wyoming
Data columns (total 41 columns):
year                                                                             50 non-null object
month                                                                            50 non-null object
permit                                                                           50 non-null int64
permit_recheck                                                                   50 non-null int64
handgun                                                                          50 non-null int64
long_gun                                                                         50 non-null int64
other                                                                            50 non-null int64
multiple                                                                         50 non-null int64
admin                                                                            50 non-null int64
prepawn_handgun                                                                  50 non-null int64
prepawn_long_gun                                                                 50 non-null int64
prepawn_other                                                                    50 non-null int64
redemption_handgun                                                               50 non-null int64
redemption_long_gun                                                              50 non-null int64
redemption_other                                                                 50 non-null int64
returned_handgun                                                                 50 non-null int64
returned_long_gun                                                                50 non-null int64
returned_other                                                                   50 non-null int64
rentals_handgun                                                                  50 non-null int64
rentals_long_gun                                                                 50 non-null int64
private_sale_handgun                                                             50 non-null int64
private_sale_long_gun                                                            50 non-null int64
private_sale_other                                                               50 non-null int64
return_to_seller_handgun                                                         50 non-null int64
return_to_seller_long_gun                                                        50 non-null int64
return_to_seller_other                                                           50 non-null int64
totals                                                                           50 non-null int64
Population_estimates_July_1_2016__(V2016)                                        50 non-null float64
Persons_under_5_years_percent_July_1_2016__(V2016)                               50 non-null float64
Persons_under_18_years_percent_July_1_2016__(V2016)                              50 non-null float64
Persons_65_years_and_over_percent__July_1_2016__(V2016)                          50 non-null float64
Female_persons_percent__July_1_2016__(V2016)                                     50 non-null float64
White_alone_percent_July_1_2016__(V2016)                                         50 non-null float64
Black_or_African_American_alone_percent_July_1_2016__(V2016)                     50 non-null float64
American_Indian_and_Alaska_Native_alone_percent_July_1_2016__(V2016)             50 non-null float64
Asian_alone_percent_July_1_2016__(V2016)                                         50 non-null float64
Native_Hawaiian_and_Other_Pacific_Islander_alone_percent_July_1_2016__(V2016)    50 non-null float64
Two_or_More_Races_percent_July_1_2016__(V2016)                                   50 non-null float64
Hispanic_or_Latino_percent_July_1_2016__(V2016)                                  50 non-null float64
White_alone_not_Hispanic_or_Latino_percent_July_1_2016__(V2016)                  50 non-null float64
Housing_units__July_1_2016__(V2016)                                              50 non-null float64
dtypes: float64(14), int64(25), object(2)
memory usage: 16.4+ KB

What census data is most associated with high gun per capita?¶

# Reformat number display to decimal and not scientific
pd.options.display.float_format = '{:.6f}'.format

# Display all columns and rows
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)

# Explore data statistics
dpl(df.describe())

# Calculates gun per capita
dfpc = pd.DataFrame(index = df.index)
for i in df:
    if i in list(gun.columns):
        if i not in ['month','year']:
            dfpc[i+'_per_capita'] = percpcap(df[i],df['Population_estimates_July_1_2016__(V2016)'])
dfpc.sample(10)

# Keep the top 5 of gun per capita in July 2016
dfpc.sort_values('totals_per_capita', ascending = False, inplace = True)
dfpc5 = dfpc.head(5) 
dfpc5

# Check for correlation between data
corrdata = pd.DataFrame(dfpc['totals_per_capita']).join(census)
pearsoncorr = corrdata.corr(method='pearson')
pearsoncorr_sorted = pearsoncorr.sort_values('totals_per_capita', ascending = False, inplace = False)
pearsoncorr_sorted

The correlation between total guns per capita and censuses are not strong, the closest are white alone percent and white alone not hispanic or latino percent, which are most of the people in the U.S., and the strongest negative correlation are asian alone percent. Let's take a deeper look here.

# Plot the correlation to have better visuals
sb.heatmap(pearsoncorr, 
            xticklabels=pearsoncorr.columns,
            yticklabels=pearsoncorr.columns,
            cmap='RdBu_r',
            annot=False,
            linewidth=0.5);

# Explore individual correlations
for i in list(census.columns):
    plt.scatter(df['totals'],df[i])
    plt.title(i)
    plt.xlabel('Totals')
    plt.ylabel(i)
    plt.show();

# Review Top 5 data
top5 = list(dfpc5.index)
for i in top5:
    dpl(census[(i):(i)].transpose().sort_values(i, ascending = False))

For Kentucky, the highest percentage of people is White.

# Time to run the notebook
elapsed_time = time.time() - start_time
print('----- Completed in {} seconds -----'.format(elapsed_time))

----- Completed in 29.517624139785767 seconds -----

Conclusions¶

After get a couple of views of the data, being focused on gun per capita, I noted that the state with highest gun per capita in July 2016 is Kentucky, being followed by Indiana, Illinois, Alabama and New Hamphshire in the top 5. Also, I looked for correlations between high gun per capita and individual census and they are not strong, which makes me conclude that this indice is not directly related with the age, race, or gender. In fact, the highest percentage of people in the top 5 of gun per capita are white alone not hispanic or latino, which is most of the people in the U.S.

from subprocess import call
call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])

	xlsx	state	permit	permit_recheck	handgun	long_gun	other	multiple	admin	prepawn_handgun	prepawn_long_gun	prepawn_other	redemption_handgun	redemption_long_gun	redemption_other	returned_handgun	returned_long_gun	returned_other	rentals_handgun	rentals_long_gun	private_sale_handgun	private_sale_long_gun	private_sale_other	return_to_seller_handgun	return_to_seller_long_gun	return_to_seller_other	totals
0	2017-09	Alabama	16717.000000	0.000000	5734.000000	6320.000000	221.000000	317	0.000000	15.000000	21.000000	2.000000	1378.000000	1262.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	9.000000	16.000000	3.000000	0.000000	0.000000	3.000000	32019
1	2017-09	Alaska	209.000000	2.000000	2320.000000	2930.000000	219.000000	160	0.000000	5.000000	2.000000	0.000000	200.000000	154.000000	2.000000	28.000000	30.000000	0.000000	0.000000	0.000000	17.000000	24.000000	1.000000	0.000000	0.000000	0.000000	6303

	xlsx	state	permit	permit_recheck	handgun	long_gun	other	multiple	admin	prepawn_handgun	prepawn_long_gun	prepawn_other	redemption_handgun	redemption_long_gun	redemption_other	returned_handgun	returned_long_gun	returned_other	rentals_handgun	rentals_long_gun	private_sale_handgun	private_sale_long_gun	private_sale_other	return_to_seller_handgun	return_to_seller_long_gun	return_to_seller_other	totals
12483	1998-11	Wisconsin	0.000000	nan	25.000000	214.000000	nan	2	0.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	241
12484	1998-11	Wyoming	8.000000	nan	45.000000	49.000000	nan	5	0.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	107

	Fact	Fact Note	Alabama	Alaska	Arizona	Arkansas	California	Colorado	Connecticut	Delaware	Florida	Georgia	Hawaii	Idaho	Illinois	Indiana	Iowa	Kansas	Kentucky	Louisiana	Maine	Maryland	Massachusetts	Michigan	Minnesota	Mississippi	Missouri	Montana	Nebraska	Nevada	New Hampshire	New Jersey	New Mexico	New York	North Carolina	North Dakota	Ohio	Oklahoma	Oregon	Pennsylvania	Rhode Island	South Carolina	South Dakota	Tennessee	Texas	Utah	Vermont	Virginia	Washington	West Virginia	Wisconsin	Wyoming
0	Population estimates, July 1, 2016, (V2016)	NaN	4,863,300	741,894	6,931,071	2,988,248	39,250,017	5,540,545	3,576,452	952,065	20,612,439	10,310,371	1,428,557	1,683,140	12,801,539	6,633,053	3,134,693	2,907,289	4,436,974	4,681,666	1,331,479	6,016,447	6,811,779	9,928,300	5,519,952	2,988,726	6,093,000	1,042,520	1,907,116	2,940,058	1,334,795	8,944,469	2081015	19745289	10146788	757952	11614373	3923561	4093465	12784227	1056426	4961119	865454	6651194	27,862,596	3,051,217	624,594	8,411,808	7,288,000	1,831,102	5,778,708	585,501
1	Population estimates base, April 1, 2010, (V2...	NaN	4,780,131	710,249	6,392,301	2,916,025	37,254,522	5,029,324	3,574,114	897,936	18,804,592	9,688,680	1,360,301	1,567,650	12,831,574	6,484,136	3,046,869	2,853,129	4,339,344	4,533,479	1,328,364	5,773,786	6,547,813	9,884,129	5,303,924	2,968,103	5,988,928	989,414	1,826,334	2,700,691	1,316,461	8,791,953	2059198	19378110	9535688	672591	11536727	3751615	3831072	12702857	1052940	4625410	814195	6346298	25,146,100	2,763,888	625,741	8,001,041	6,724,545	1,853,011	5,687,289	563,767

	Fact	Fact Note	Alabama	Alaska	Arizona	Arkansas	California	Colorado	Connecticut	Delaware	Florida	Georgia	Hawaii	Idaho	Illinois	Indiana	Iowa	Kansas	Kentucky	Louisiana	Maine	Maryland	Massachusetts	Michigan	Minnesota	Mississippi	Missouri	Montana	Nebraska	Nevada	New Hampshire	New Jersey	New Mexico	New York	North Carolina	North Dakota	Ohio	Oklahoma	Oregon	Pennsylvania	Rhode Island	South Carolina	South Dakota	Tennessee	Texas	Utah	Vermont	Virginia	Washington	West Virginia	Wisconsin	Wyoming
0	Population estimates, July 1, 2016, (V2016)	NaN	4,863,300	741,894	6,931,071	2,988,248	39,250,017	5,540,545	3,576,452	952,065	20,612,439	10,310,371	1,428,557	1,683,140	12,801,539	6,633,053	3,134,693	2,907,289	4,436,974	4,681,666	1,331,479	6,016,447	6,811,779	9,928,300	5,519,952	2,988,726	6,093,000	1,042,520	1,907,116	2,940,058	1,334,795	8,944,469	2081015	19745289	10146788	757952	11614373	3923561	4093465	12784227	1056426	4961119	865454	6651194	27,862,596	3,051,217	624,594	8,411,808	7,288,000	1,831,102	5,778,708	585,501
1	Population estimates base, April 1, 2010, (V2...	NaN	4,780,131	710,249	6,392,301	2,916,025	37,254,522	5,029,324	3,574,114	897,936	18,804,592	9,688,680	1,360,301	1,567,650	12,831,574	6,484,136	3,046,869	2,853,129	4,339,344	4,533,479	1,328,364	5,773,786	6,547,813	9,884,129	5,303,924	2,968,103	5,988,928	989,414	1,826,334	2,700,691	1,316,461	8,791,953	2059198	19378110	9535688	672591	11536727	3751615	3831072	12702857	1052940	4625410	814195	6346298	25,146,100	2,763,888	625,741	8,001,041	6,724,545	1,853,011	5,687,289	563,767
2	Population, percent change - April 1, 2010 (es...	NaN	1.70%	4.50%	8.40%	2.50%	5.40%	10.20%	0.10%	6.00%	9.60%	6.40%	5.00%	7.40%	-0.20%	2.30%	2.90%	1.90%	2.20%	3.30%	0.20%	4.20%	4.00%	0.40%	4.10%	0.70%	1.70%	5.40%	4.40%	8.90%	1.40%	1.70%	0.011	0.019	0.064	0.127	0.007	0.046	0.068	0.006	0.003	0.073	0.063	0.048	10.80%	10.40%	-0.20%	5.10%	8.40%	-1.20%	1.60%	3.90%
3	Population, Census, April 1, 2010	NaN	4,779,736	710,231	6,392,017	2,915,918	37,253,956	5,029,196	3,574,097	897,934	18,801,310	9,687,653	1,360,301	1,567,582	12,830,632	6,483,802	3,046,355	2,853,118	4,339,367	4,533,372	1,328,361	5,773,552	6,547,629	9,883,640	5,303,925	2,967,297	5,988,927	989,415	1,826,341	2,700,551	1,316,470	8,791,894	2059179	19378102	9535483	672591	11536504	3751351	3831074	12702379	1052567	4625364	814180	6346105	25,145,561	2,763,885	625,741	8,001,024	6,724,540	1,852,994	5,686,986	563,626
4	Persons under 5 years, percent, July 1, 2016, ...	NaN	6.00%	7.30%	6.30%	6.40%	6.30%	6.10%	5.20%	5.80%	5.50%	6.40%	6.40%	6.80%	6.00%	6.40%	6.40%	6.70%	6.20%	6.60%	4.90%	6.10%	5.30%	5.80%	6.40%	6.30%	6.10%	6.00%	7.00%	6.30%	4.80%	5.80%	0.062	0.059	0.06	0.073	0.06	0.068	0.058	0.056	0.052	0.059	0.071	0.061	7.20%	8.30%	4.90%	6.10%	6.20%	5.50%	5.80%	6.50%
5	Persons under 5 years, percent, April 1, 2010	NaN	6.40%	7.60%	7.10%	6.80%	6.80%	6.80%	5.70%	6.20%	5.70%	7.10%	6.40%	7.80%	6.50%	6.70%	6.60%	7.20%	6.50%	6.90%	5.20%	6.30%	5.60%	6.00%	6.70%	7.10%	6.50%	6.30%	7.20%	6.90%	5.30%	6.20%	0.07	0.06	0.066	0.066	0.062	0.07	0.062	0.057	0.055	0.065	0.073	0.064	7.70%	9.50%	5.10%	6.40%	6.50%	5.60%	6.30%	7.10%
6	Persons under 18 years, percent, July 1, 2016,...	NaN	22.60%	25.20%	23.50%	23.60%	23.20%	22.80%	21.10%	21.50%	20.10%	24.40%	21.60%	26.00%	22.90%	23.80%	23.30%	24.60%	22.80%	23.80%	19.10%	22.40%	20.20%	22.10%	23.30%	24.10%	22.80%	21.80%	24.80%	23.00%	19.50%	22.20%	0.236	0.212	0.227	0.233	0.225	0.245	0.212	0.209	0.197	0.221	0.246	0.226	26.20%	30.20%	19.00%	22.20%	22.40%	20.50%	22.30%	23.70%
7	Persons under 18 years, percent, April 1, 2010	NaN	23.70%	26.40%	25.50%	24.40%	25.00%	24.40%	22.90%	22.90%	21.30%	25.70%	22.30%	27.40%	24.40%	24.80%	23.90%	25.50%	23.60%	24.70%	20.70%	23.40%	21.70%	23.70%	24.20%	25.50%	23.80%	22.60%	25.10%	24.60%	21.80%	23.50%	0.252	0.223	0.239	0.223	0.237	0.248	0.226	0.22	0.213	0.234	0.249	0.236	27.30%	31.50%	20.70%	23.20%	23.50%	20.90%	23.60%	24.00%
8	Persons 65 years and over, percent, July 1, 2...	NaN	16.10%	10.40%	16.90%	16.30%	13.60%	13.40%	16.10%	17.50%	19.90%	13.10%	17.10%	15.10%	14.60%	14.90%	16.40%	15.00%	15.60%	14.40%	19.40%	14.60%	15.80%	16.20%	15.10%	15.10%	16.10%	17.70%	15.00%	15.00%	17.00%	15.30%	0.165	0.154	0.155	0.145	0.162	0.15	0.168	0.174	0.165	0.167	0.16	0.157	12.00%	10.50%	18.10%	14.60%	14.80%	18.80%	16.10%	15.00%
9	Persons 65 years and over, percent, April 1, 2010	NaN	13.80%	7.70%	13.80%	14.40%	11.40%	10.90%	14.20%	14.40%	17.30%	10.70%	14.30%	12.40%	12.50%	13.00%	14.90%	13.20%	13.30%	12.30%	15.90%	12.30%	13.80%	13.80%	12.90%	12.80%	14.00%	14.80%	13.50%	12.00%	13.50%	13.50%	0.132	0.135	0.129	0.145	0.141	0.135	0.139	0.154	0.144	0.137	0.143	0.134	10.30%	9.00%	14.60%	12.20%	12.30%	16.00%	13.70%	12.40%
10	Female persons, percent, July 1, 2016, (V2016)	NaN	51.60%	47.70%	50.30%	50.90%	50.30%	49.70%	51.20%	51.60%	51.10%	51.30%	49.80%	49.90%	50.90%	50.70%	50.30%	50.20%	50.70%	51.10%	51.00%	51.60%	51.50%	50.80%	50.20%	51.50%	50.90%	49.70%	50.20%	49.90%	50.50%	51.20%	0.505	0.514	0.514	0.487	0.51	0.505	0.505	0.51	0.514	0.515	0.496	0.512	50.40%	49.70%	50.60%	50.80%	50.00%	50.50%	50.30%	48.90%
11	Female persons, percent, April 1, 2010	NaN	51.50%	48.00%	50.30%	50.90%	50.30%	49.90%	51.30%	51.60%	51.10%	51.20%	49.90%	49.90%	51.00%	50.80%	50.50%	50.40%	50.80%	51.00%	51.10%	51.60%	51.60%	50.90%	50.40%	51.40%	51.00%	49.80%	50.40%	49.50%	50.70%	51.30%	0.506	0.516	0.513	0.495	0.512	0.505	0.505	0.513	0.517	0.514	0.5	0.513	50.40%	49.80%	50.70%	50.90%	50.20%	50.70%	50.40%	49.00%
12	White alone, percent, July 1, 2016, (V2016)	(a)	69.30%	66.10%	83.30%	79.40%	72.70%	87.50%	80.60%	70.10%	77.60%	61.20%	25.80%	93.30%	77.20%	85.60%	91.40%	86.60%	88.00%	63.20%	94.80%	59.30%	81.80%	79.60%	85.00%	59.30%	83.20%	89.20%	88.90%	75.10%	93.80%	72.40%	0.826	0.699	0.71	0.879	0.825	0.746	0.874	0.824	0.844	0.685	0.852	0.787	79.40%	91.10%	94.60%	70.00%	80.00%	93.60%	87.50%	92.80%
13	Black or African American alone, percent, July...	(a)	26.80%	3.80%	4.90%	15.70%	6.50%	4.50%	11.80%	22.60%	16.80%	32.00%	2.20%	0.80%	14.70%	9.70%	3.70%	6.20%	8.30%	32.60%	1.50%	30.70%	8.60%	14.20%	6.20%	37.70%	11.80%	0.60%	5.00%	9.60%	1.50%	15.00%	0.025	0.177	0.222	0.029	0.128	0.078	0.021	0.118	0.081	0.275	0.02	0.171	12.60%	1.40%	1.30%	19.80%	4.10%	3.60%	6.60%	1.30%
14	American Indian and Alaska Native alone, perce...	(a)	0.70%	15.20%	5.40%	1.00%	1.70%	1.60%	0.50%	0.60%	0.50%	0.50%	0.40%	1.80%	0.60%	0.40%	0.50%	1.20%	0.30%	0.80%	0.70%	0.60%	0.50%	0.70%	1.30%	0.60%	0.60%	6.60%	1.40%	1.60%	0.30%	0.60%	0.106	0.01	0.016	0.055	0.003	0.092	0.018	0.004	0.01	0.005	0.09	0.004	1.00%	1.60%	0.40%	0.50%	1.90%	0.20%	1.10%	2.70%
15	Asian alone, percent, July 1, 2016, (V2016)	(a)	1.40%	6.30%	3.40%	1.60%	14.80%	3.30%	4.70%	4.00%	2.90%	4.10%	37.70%	1.50%	5.50%	2.20%	2.50%	3.00%	1.50%	1.80%	1.20%	6.60%	6.70%	3.10%	4.90%	1.10%	2.00%	0.80%	2.50%	8.70%	2.70%	9.80%	0.017	0.089	0.029	0.015	0.022	0.022	0.045	0.035	0.036	0.016	0.015	0.018	4.80%	2.50%	1.80%	6.60%	8.60%	0.80%	2.80%	1.00%
16	Native Hawaiian and Other Pacific Islander alo...	(a)	0.10%	1.30%	0.30%	0.30%	0.50%	0.20%	0.10%	0.10%	0.10%	0.10%	10.20%	0.20%	0.10%	0.10%	0.10%	0.10%	0.10%	0.10%	Z	0.10%	0.10%	Z	0.10%	0.10%	0.10%	0.10%	0.10%	0.80%	0.10%	0.10%	0.002	0.001	0.001	0.001	0.001	0.002	0.004	0.001	0.002	0.001	0.001	0.001	0.10%	1.00%	Z	0.10%	0.80%	Z	0.10%	0.10%
17	Two or More Races, percent, July 1, 2016, (V2...	NaN	1.60%	7.30%	2.80%	2.00%	3.80%	3.00%	2.30%	2.60%	2.10%	2.10%	23.70%	2.40%	1.90%	2.00%	1.80%	2.90%	1.90%	1.60%	1.70%	2.80%	2.30%	2.40%	2.40%	1.20%	2.20%	2.70%	2.10%	4.20%	1.70%	2.20%	0.025	0.025	0.022	0.021	0.022	0.061	0.038	0.019	0.027	0.018	0.023	0.019	1.90%	2.50%	1.90%	2.90%	4.60%	1.70%	1.90%	2.10%
18	Hispanic or Latino, percent, July 1, 2016, (V...	(b)	4.20%	7.00%	30.90%	7.30%	38.90%	21.30%	15.70%	9.20%	24.90%	9.40%	10.40%	12.30%	17.00%	6.80%	5.80%	11.60%	3.50%	5.00%	1.60%	9.80%	11.50%	5.00%	5.20%	3.10%	4.10%	3.60%	10.70%	28.50%	3.50%	20.00%	0.485	0.19	0.092	0.036	0.037	0.103	0.128	0.07	0.149	0.055	0.037	0.052	39.10%	13.80%	1.90%	9.10%	12.40%	1.50%	6.70%	10.00%
19	White alone, not Hispanic or Latino, percent, ...	NaN	65.80%	61.20%	55.50%	72.90%	37.70%	68.60%	67.70%	62.90%	54.90%	53.40%	22.10%	82.40%	61.70%	79.60%	86.20%	76.30%	85.00%	59.00%	93.50%	51.50%	73.00%	75.40%	80.60%	56.90%	79.70%	86.50%	79.60%	49.90%	90.80%	55.80%	0.381	0.558	0.635	0.85	0.795	0.662	0.764	0.77	0.733	0.639	0.825	0.742	42.60%	78.80%	93.10%	62.40%	69.50%	92.30%	81.70%	84.10%
20	Veterans, 2011-2015	NaN	363,170	69,323	505,794	220,953	1,777,410	391,725	199,331	71,213	1,507,738	670,617	110,238	119,711	668,933	426,493	211,066	198,396	297,312	281,989	119,058	403,900	355,083	626,722	342,388	184,774	451,342	90,000	132,918	220,332	106,827	393,277	164157	828586	696119	51179	806531	295847	306723	870770	66076	378959	63742	462414	1,539,655	134,332	44,708	706,539	564,864	150,021	381,940	48,505
21	Foreign born persons, percent, 2011-2015	NaN	3.50%	7.40%	13.50%	4.70%	27.00%	9.80%	13.90%	8.70%	19.70%	9.80%	17.70%	6.10%	14.00%	4.80%	4.70%	6.90%	3.40%	4.00%	3.50%	14.50%	15.50%	6.30%	7.70%	2.30%	3.90%	2.10%	6.60%	19.20%	5.70%	21.70%	0.098	0.225	0.077	0.032	0.041	0.058	0.099	0.063	0.133	0.048	0.03	0.048	16.60%	8.40%	4.30%	11.70%	13.40%	1.50%	4.80%	3.60%
22	Housing units, July 1, 2016, (V2016)	NaN	2,230,185	310,658	2,961,003	1,354,762	14,060,525	2,339,118	1,499,116	426,149	9,301,642	4,218,776	537,114	700,825	5,326,970	2,854,546	1,380,162	1,259,864	1,965,556	2,036,975	730,705	2,447,127	2,858,026	4,560,055	2,409,935	1,307,441	2,760,084	497,756	827,156	1,221,698	625,307	3,604,409	917568	8231687	4540498	368624	5164361	1721045	1732786	5612002	462589	2236153	383838	2919671	10,753,629	1,054,164	329,525	3,491,054	3,025,685	886,640	2,668,444	270,600
23	Housing units, April 1, 2010	NaN	2,171,853	306,967	2,844,526	1,316,299	13,680,081	2,212,898	1,487,891	405,885	8,989,580	4,088,801	519,508	667,796	5,296,715	2,795,541	1,336,417	1,233,215	1,927,164	1,964,981	721,830	2,378,814	2,808,254	4,532,233	2,347,201	1,274,719	2,712,729	482,825	796,793	1,173,814	614,754	3,553,562	901388	8108103	4327528	317498	5127508	1664378	1675562	5567315	463388	2137683	363438	2812133	9,977,436	979,709	322,539	3,364,939	2,885,677	881,917	2,624,358	261,868
24	Owner-occupied housing unit rate, 2011-2015	NaN	68.70%	63.40%	62.80%	66.10%	54.30%	64.30%	67.00%	71.20%	65.30%	63.30%	56.90%	68.90%	66.40%	69.00%	71.50%	66.70%	67.20%	65.80%	71.20%	66.80%	62.10%	71.00%	71.70%	68.40%	67.20%	67.20%	66.20%	55.10%	71.00%	64.50%	0.681	0.536	0.651	0.641	0.663	0.661	0.613	0.692	0.601	0.686	0.681	0.668	62.20%	69.50%	71.00%	66.20%	62.50%	72.50%	67.30%	69.10%
25	Median value of owner-occupied housing units, ...	NaN	$125,500	$250,000	$167,500	$111,400	$385,500	$247,800	$270,500	$231,500	$159,000	$148,100	$515,300	$162,900	$173,800	$124,200	$129,200	$132,000	$123,200	$144,100	$173,800	$286,900	$333,100	$122,400	$186,200	$103,100	$138,400	$193,500	$133,200	$173,700	$237,300	$315,900	160300	283400	154900	153800	129900	117900	237300	166000	238000	139900	140500	142100	$136,000	$215,900	$217,500	$245,000	$259,500	$103,800	$165,800	$194,800
26	Median selected monthly owner costs -with a mo...	NaN	$1,139	$1,827	$1,343	$1,019	$2,155	$1,577	$2,067	$1,537	$1,435	$1,346	$2,270	$1,189	$1,636	$1,102	$1,171	$1,282	$1,112	$1,200	$1,336	$1,951	$2,063	$1,257	$1,490	$1,077	$1,210	$1,294	$1,270	$1,442	$1,870	$2,386	1227	2022	1248	1243	1246	1147	1557	1425	1815	1183	1210	1181	$1,432	$1,428	$1,535	$1,711	$1,731	$966	$1,402	$1,348
27	Median selected monthly owner costs -without a...	NaN	$345	$554	$380	$327	$500	$419	$833	$445	$463	$392	$505	$340	$574	$378	$434	$455	$339	$317	$477	$582	$717	$461	$479	$341	$402	$387	$471	$410	$747	$973	327	709	373	423	444	370	469	498	682	347	433	359	$460	$388	$641	$433	$511	$293	$532	$386
28	Median gross rent, 2011-2015	NaN	$717	$1,146	$913	$677	$1,255	$1,002	$1,075	$1,018	$1,002	$879	$1,438	$743	$907	$745	$697	$757	$675	$788	$777	$1,230	$1,102	$783	$848	$717	$746	$711	$726	$973	$1,000	$1,192	777	1132	797	709	730	727	907	840	925	790	655	764	$882	$887	$895	$1,116	$1,014	$643	$776	$789
29	Building permits, 2016	NaN	15,001	1,503	35,578	9,474	102,350	38,974	5,504	5,804	116,240	51,675	3,369	12,165	22,603	18,713	14,317	9,807	12,714	14,503	4,010	17,044	16,288	20,408	21,449	6,886	18,997	4,781	8,078	17,952	3,796	26,793	4863	33711	60550	3981	22816	12092	19586	23303	1226	32165	5686	36157	165,853	22,662	1,771	31,132	44,077	2,544	19,274	1,727
30	Households, 2011-2015	NaN	1,848,325	250,969	2,412,212	1,138,025	12,717,801	2,024,468	1,352,583	344,022	7,300,494	3,574,362	450,572	589,320	4,786,388	2,501,937	1,236,409	1,113,472	1,708,499	1,727,919	553,284	2,166,389	2,549,721	3,841,148	2,124,745	1,096,593	2,364,688	409,394	736,613	1,016,709	520,251	3,189,486	763603	7262279	3775581	299638	4585084	1455321	1533430	4958859	410602	1815094	330858	2504556	9,149,196	906,292	257,167	3,062,783	2,668,912	740,890	2,299,107	226,865
31	Persons per household, 2011-2015	NaN	2.55	2.81	2.69	2.53	2.96	2.55	2.57	2.62	2.63	2.73	3.02	2.69	2.63	2.55	2.42	2.53	2.5	2.6	2.34	2.67	2.53	2.52	2.49	2.64	2.48	2.41	2.47	2.72	2.47	2.73	2.67	2.63	2.54	2.32	2.46	2.57	2.51	2.49	2.46	2.56	2.45	2.53	2.84	3.15	2.34	2.62	2.56	2.43	2.43	2.49
32	Living in same house 1 year ago, percent of pe...	NaN	85.30%	80.90%	81.30%	83.90%	85.20%	80.80%	87.80%	86.60%	83.80%	84.00%	85.10%	82.40%	86.80%	85.00%	84.80%	83.50%	84.70%	86.30%	85.90%	86.60%	87.10%	85.30%	85.50%	86.10%	83.90%	83.60%	83.70%	78.80%	86.50%	90.20%	0.857	0.891	0.847	0.822	0.853	0.826	0.819	0.88	0.866	0.847	0.834	0.847	83.30%	82.90%	86.50%	84.40%	82.50%	88.20%	85.80%	81.90%
33	Language other than English spoken at home, pe...	NaN	5.20%	16.20%	26.90%	7.20%	43.90%	16.90%	21.90%	12.90%	28.10%	13.60%	25.20%	10.60%	22.70%	8.30%	7.40%	11.30%	5.10%	8.50%	6.60%	17.20%	22.50%	9.20%	11.00%	3.90%	6.00%	4.10%	10.80%	30.00%	7.90%	30.50%	0.357	0.304	0.112	0.056	0.067	0.098	0.151	0.106	0.214	0.069	0.065	0.069	35.00%	14.70%	5.40%	15.40%	18.90%	2.50%	8.70%	7.10%
34	High school graduate or higher, percent of per...	NaN	84.30%	92.10%	86.00%	84.80%	81.80%	90.70%	89.90%	88.40%	86.90%	85.40%	91.00%	89.50%	87.90%	87.80%	91.50%	90.20%	84.20%	83.40%	91.60%	89.40%	89.80%	89.60%	92.40%	82.30%	88.40%	92.80%	90.70%	85.10%	92.30%	88.60%	0.842	0.856	0.858	0.917	0.891	0.869	0.898	0.892	0.862	0.856	0.909	0.855	81.90%	91.20%	91.80%	88.30%	90.40%	85.00%	91.00%	92.30%
35	Bachelor's degree or higher, percent of person...	NaN	23.50%	28.00%	27.50%	21.10%	31.40%	38.10%	37.60%	30.00%	27.30%	28.80%	30.80%	25.90%	32.30%	24.10%	26.70%	31.00%	22.30%	22.50%	29.00%	37.90%	40.50%	26.90%	33.70%	20.70%	27.10%	29.50%	29.30%	23.00%	34.90%	36.80%	0.263	0.342	0.284	0.277	0.261	0.241	0.308	0.286	0.319	0.258	0.27	0.249	27.60%	31.10%	36.00%	36.30%	32.90%	19.20%	27.80%	25.70%
36	With a disability, under age 65 years, percent...	NaN	11.80%	8.10%	8.20%	12.30%	6.80%	7.20%	7.10%	8.50%	8.50%	8.80%	6.50%	9.00%	7.10%	9.70%	7.90%	8.60%	12.90%	11.00%	11.90%	7.10%	7.90%	10.30%	7.10%	11.90%	10.40%	9.10%	7.30%	9.00%	8.50%	6.60%	0.101	0.074	0.096	0.068	0.099	0.113	0.102	0.095	0.089	0.103	0.084	0.112	8.10%	6.60%	10.00%	7.70%	8.90%	14.40%	8.20%	8.50%
37	Persons without health insurance, under age 6...	NaN	10.70%	15.50%	11.90%	9.30%	8.30%	8.60%	5.70%	6.60%	15.30%	14.80%	4.20%	11.80%	7.40%	9.40%	5.00%	10.10%	6.00%	11.90%	9.90%	7.00%	2.90%	6.30%	4.80%	13.90%	10.50%	9.80%	9.90%	13.10%	7.10%	9.20%	0.108	0.07	0.122	0.081	0.066	0.161	0.073	0.067	0.051	0.119	0.103	0.106	18.60%	9.70%	4.50%	10.10%	6.90%	6.50%	6.20%	13.40%
38	In civilian labor force, total, percent of pop...	NaN	58.00%	67.70%	59.30%	58.50%	63.10%	67.60%	67.20%	63.10%	58.80%	62.30%	61.50%	62.60%	65.60%	63.90%	67.60%	66.30%	59.10%	60.40%	63.40%	67.90%	67.50%	61.20%	70.00%	57.50%	63.20%	63.60%	69.90%	64.00%	68.20%	65.90%	0.591	0.633	0.618	0.693	0.633	0.611	0.621	0.628	0.655	0.601	0.686	0.61	64.30%	67.70%	66.60%	64.70%	63.50%	53.90%	67.10%	67.70%
39	In civilian labor force, female, percent of po...	NaN	53.00%	65.00%	54.50%	53.30%	57.20%	62.60%	62.80%	59.40%	54.70%	57.90%	58.80%	56.80%	60.70%	59.00%	63.30%	61.60%	54.90%	56.00%	59.90%	64.10%	63.40%	57.10%	66.20%	53.70%	59.30%	59.50%	65.20%	59.30%	63.80%	60.60%	0.549	0.587	0.577	0.65	0.59	0.557	0.576	0.583	0.616	0.562	0.646	0.563	57.70%	59.50%	63.60%	60.80%	58.50%	49.20%	63.50%	62.70%
40	Total accommodation and food services sales, 2...	(c)	7,576,462	2,221,335	13,996,635	4,307,264	90,830,372	13,617,654	9,542,068	2,148,437	49,817,925	18,976,611	9,536,706	2,680,225	27,937,381	13,076,602	5,468,687	4,873,411	7,500,115	11,697,949	2,901,347	12,516,782	17,508,975	17,962,356	11,722,627	6,999,175	12,430,310	2,420,455	3,094,498	27,481,505	2,942,278	19,673,558	4349672	49285508	18622258	2045123	20652777	7121153	8466788	23504192	2481314	9763818	1873699	12499013	54,480,811	4,789,281	1,564,272	17,795,901	14,297,278	4,036,333	10,303,256	1,644,844
41	Total health care and social assistance receip...	(c)	26,039,632	6,375,483	37,055,881	15,792,628	248,953,592	29,488,161	29,573,119	7,003,251	124,061,425	51,800,643	8,136,947	7,895,614	83,431,778	42,493,117	18,583,828	18,248,370	26,264,745	27,951,792	10,297,043	40,821,901	63,583,090	63,018,786	40,403,572	16,630,587	40,089,316	6,469,475	12,869,418	13,928,514	9,616,460	60,375,232	11236630	155666053	55227505	5418355	80915693	22795438	24956816	96329248	8223005	22941292	6211731	42383683	145,035,130	14,521,857	4,457,996	47,705,003	43,966,889	12,259,395	40,680,625	3,291,478
42	Total manufacturers shipments, 2012 ($1,000)	(c)	124,809,759	D	51,243,473	62,712,925	512,303,164	50,447,098	55,160,095	22,597,384	96,924,106	155,836,792	D	20,201,432	281,037,755	242,763,842	116,668,830	86,076,260	129,284,438	271,191,050	16,044,543	39,532,989	81,927,799	238,892,383	123,076,309	66,441,608	111,535,362	11,535,236	57,499,177	14,719,141	18,895,624	108,854,971	29102352	148879931	202344646	14427360	313629976	74295394	51349948	231396178	11262158	99160840	16882647	139960482	702,603,073	50,046,429	9,315,494	96,389,872	131,530,601	24,553,072	177,728,926	10,783,794
43	Total merchant wholesaler sales, 2012 ($1,000)	(c)	57,746,565	5,216,303	69,437,272	31,256,110	666,652,186	77,034,971	161,962,244	5,628,914	252,626,608	143,645,290	9,607,982	17,906,012	295,456,992	81,173,422	62,318,301	60,226,324	71,745,899	68,012,838	12,961,262	60,734,191	123,904,370	115,704,903	104,485,117	28,302,952	91,916,351	12,645,824	42,618,995	19,841,701	18,029,179	288,467,844	10720363	341734970	105275586	28150837	155426023	71892946	48325261	191170147	22310389	45520900	20411059	111718421	691,242,607	30,927,885	6,450,076	86,613,641	83,313,366	14,295,437	77,066,883	5,597,891
44	Total retail sales, 2012 ($1,000)	(c)	58,564,965	10,474,275	84,716,542	36,815,256	481,800,461	67,815,200	51,632,467	14,456,001	273,867,145	119,801,495	18,901,745	20,444,278	166,634,514	85,857,962	44,905,624	38,276,461	54,869,978	61,396,364	21,521,714	76,379,707	92,915,380	119,302,046	78,898,182	37,053,190	90,546,581	15,623,573	30,470,717	38,234,170	26,018,201	133,665,728	25179274	251167736	120691007	15519816	153553997	50256231	49481054	178794945	12063865	58093824	13791827	91641605	356,116,376	38,024,486	9,933,751	110,002,385	118,924,049	22,637,923	78,201,822	9,446,043
45	Total retail sales per capita, 2012	(c)	$12,145	$14,320	$12,927	$12,483	$12,665	$13,073	$14,381	$15,763	$14,177	$12,077	$13,576	$12,812	$12,942	$13,133	$14,607	$13,263	$12,526	$13,342	$16,192	$12,980	$13,980	$12,071	$14,667	$12,413	$15,036	$15,544	$16,422	$13,858	$19,700	$15,079	12073	12834	12376	22183	13301	13174	12690	14008	11486	12298	16550	14194	$13,666	$13,317	$15,868	$13,438	$17,243	$12,201	$13,656	$16,388
46	Mean travel time to work (minutes), workers ag...	NaN	24.4	19	24.8	21.6	28	24.8	25.4	25.5	26.4	27.4	26.8	20.2	28.4	23.3	18.9	19.1	23	25.1	23.6	32.3	28.7	24.2	23.1	24.1	23.2	18.1	18.3	23.9	26.9	31	21.6	32.3	23.9	17.2	23.2	21.3	22.9	26.3	24.2	23.9	16.9	24.5	25.6	21.6	22.5	27.9	26.3	25.6	21.9	18.3
47	Median household income (in 2015 dollars), 201...	NaN	$43,623	$72,515	$50,255	$41,371	$61,818	$60,629	$70,331	$60,509	$47,507	$49,620	$69,515	$47,583	$57,574	$49,255	$53,183	$52,205	$43,740	$45,047	$49,331	$74,551	$68,563	$49,576	$61,492	$39,665	$48,173	$47,169	$52,997	$51,847	$66,779	$72,093	44963	59269	46868	57181	49429	46879	51243	53599	56852	45483	50957	45219	$53,207	$60,727	$55,176	$65,015	$61,062	$41,751	$53,357	$58,840
48	Per capita income in past 12 months (in 2015 d...	NaN	$24,091	$33,413	$25,848	$22,798	$30,318	$32,217	$38,803	$30,554	$26,829	$25,737	$29,822	$23,399	$30,494	$25,346	$27,950	$27,706	$24,063	$24,981	$27,655	$36,897	$36,895	$26,607	$32,157	$21,057	$26,259	$26,381	$27,882	$26,541	$34,362	$36,582	24012	33236	25920	32035	26953	25032	27684	29291	31118	24604	26747	25227	$26,999	$24,686	$29,894	$34,152	$31,762	$23,450	$28,340	$29,803
49	Persons in poverty, percent	NaN	17.10%	9.90%	16.40%	17.20%	14.30%	11.00%	9.80%	11.70%	14.70%	16.00%	9.30%	14.40%	13.00%	14.10%	11.80%	12.10%	18.50%	20.20%	12.50%	9.70%	10.40%	15.00%	9.90%	20.80%	14.00%	13.30%	11.40%	13.80%	7.30%	10.40%	0.198	0.147	0.154	0.107	0.146	0.163	0.133	0.129	0.128	0.153	0.133	0.158	15.60%	10.20%	11.90%	11.00%	11.30%	17.90%	11.80%	11.30%
50	Total employer establishments, 2015	Includes data not distributed by county.	98,540	20,907	136,352	65,175	908,120	161,737	89,232	24,852	532,830	224,593	31,915	44,757	318,266	145,116	80,952	74,526	91,845	105,575	40,801	137,204	175,225	219,627	148,666	58,662	158,191	37,270	53,719	63,383	37,669	230,961	43793	540298	223209	24848	251668	93093	112393	299695	28387	103973	26511	133344	569,091	75,463	21,121	197,384	182,913	36,993	139,500	21,040
51	Total employment, 2015	Includes data not distributed by county.	1,634,391	267,999	2,295,186	1,003,113	14,325,377	2,253,795	1,503,102	397,385	7,777,990	3,692,490	523,677	546,524	5,427,549	2,660,503	1,338,418	1,189,876	1,579,477	1,724,973	500,549	2,239,817	3,167,329	3,725,280	2,612,314	926,391	2,442,316	375,041	870,279	1,129,965	576,424	3,558,619	626284	7998994	3670284	365893	4719985	1370988	1498727	5306896	425748	1662251	353540	2507205	10,239,710	1,203,954	266,363	3,198,718	2,602,408	565,435	2,503,532	219,881
52	Total annual payroll, 2015 ($1,000)	Includes data not distributed by county.	67,370,353	15,643,303	102,671,393	39,451,191	856,954,246	117,539,555	92,555,072	21,305,227	337,074,525	174,839,354	22,068,393	21,119,617	289,183,546	113,315,432	55,079,743	51,259,676	63,741,066	76,684,760	20,413,159	118,496,858	198,026,611	174,873,962	133,092,716	33,948,151	109,135,528	14,227,065	36,967,924	46,595,673	28,076,320	209,072,015	25145307	513083321	164936258	17184756	213161303	59124620	71006598	257626942	19704826	66120263	13812997	110481280	521,095,797	51,453,266	10,615,093	165,788,897	149,258,789	22,159,084	112,406,494	10,094,010
53	Total employment, percent change, 2014-2015	Includes data not distributed by county.	1.90%	0.40%	2.40%	1.10%	3.50%	3.30%	1.20%	1.50%	4.50%	4.00%	0.90%	3.00%	2.20%	2.20%	1.70%	1.00%	2.90%	0.40%	1.60%	1.00%	2.60%	3.20%	1.80%	1.60%	1.60%	3.10%	2.30%	3.70%	2.30%	0.90%	0.039	0.018	0.031	0.014	0.018	0.008	0.038	0.01	0.01	0.028	0.016	0.022	3.20%	4.90%	2.00%	1.20%	2.90%	-1.70%	2.20%	FN
54	Total nonemployer establishments, 2015	NaN	322,025	55,521	451,951	198,380	3,206,958	480,847	272,809	60,734	2,040,339	870,897	104,707	122,221	968,330	403,546	207,167	195,777	280,835	358,223	113,012	475,518	514,298	700,254	397,378	211,955	402,091	86,969	131,518	205,980	104,437	670,765	121279	1685636	722639	53263	763418	276218	278839	811890	76747	329431	64006	495703	2,205,149	216,280	60,312	576,446	444,135	88,136	341,935	48,140
55	All firms, 2012	NaN	374,153	68,032	499,926	231,959	3,548,449	547,352	326,693	73,418	2,100,187	929,864	118,454	146,642	1,135,017	479,059	259,121	239,118	331,546	414,291	139,570	531,953	607,664	834,087	489,494	235,454	491,606	112,419	164,089	227,156	131,638	792,088	151363	2008988	805985	68270	904814	327229	339305	975453	94642	367726	81314	550453	2,356,748	251,419	75,827	653,193	541,522	114,435	432,980	62,427
56	Men-owned firms, 2012	NaN	203,604	35,402	245,243	123,158	1,852,580	284,554	187,845	38,328	1,084,885	480,578	60,409	70,438	609,648	253,533	135,382	125,169	184,154	215,111	79,243	276,630	357,158	446,128	268,710	125,079	257,948	55,913	83,696	114,057	76,716	464,592	71132	1139910	435677	37016	510078	174395	165691	579400	54651	202446	42418	302249	1,251,696	132,163	41,270	353,012	262,650	63,112	236,252	30,039
57	Women-owned firms, 2012	NaN	137,630	22,141	182,425	75,962	1,320,085	194,508	106,678	23,964	807,817	376,506	44,453	45,121	417,500	162,798	82,345	77,194	106,011	151,114	42,067	209,119	199,210	306,986	157,821	89,159	162,616	35,449	51,936	82,508	38,525	252,944	59044	725709	287058	20316	306824	105168	123015	304803	30484	131856	23722	195694	866,678	76,269	23,417	236,290	187,677	39,065	133,859	19,344
58	Minority-owned firms, 2012	NaN	92,219	13,688	135,313	35,982	1,619,857	85,849	56,113	14,440	926,112	371,588	74,208	10,592	311,684	61,252	14,707	26,127	27,258	126,100	4,339	203,394	89,967	158,946	47,302	74,824	61,035	5,578	14,571	71,864	6,111	237,242	60622	709021	183380	3190	122653	64875	41456	131512	14737	83233	4101	105234	1,070,392	24,423	2,354	185,043	92,807	5,777	40,507	4,077
59	Nonminority-owned firms, 2012	NaN	272,651	51,147	344,981	189,029	1,819,107	442,365	259,614	54,782	1,121,749	538,893	38,510	130,973	795,129	405,090	236,561	204,562	296,155	277,676	131,322	314,902	499,959	657,237	428,716	155,094	415,972	102,746	144,122	144,944	121,297	533,808	83857	1248304	603182	62271	759569	249027	285028	818858	77042	276269	74228	434025	1,224,845	218,826	70,491	450,109	426,697	104,785	379,934	55,397
60	Veteran-owned firms, 2012	NaN	41,943	7,953	46,780	25,915	252,377	51,722	31,056	7,206	185,756	96,787	11,148	12,804	89,110	45,174	25,889	21,610	33,208	42,211	15,689	50,976	58,339	71,861	45,582	26,789	49,217	11,486	16,693	23,049	16,119	57,996	14096	137532	86571	6584	91316	36273	30918	97969	9904	47987	8604	59379	213,590	18,754	8,237	76,434	49,331	12,912	39,830	6,470
61	Nonveteran-owned firms, 2012	NaN	316,984	56,091	427,582	192,988	3,176,341	469,524	281,182	60,318	1,846,686	800,585	101,334	124,314	1,006,885	412,543	217,779	203,401	282,704	354,460	115,839	462,232	525,667	733,517	419,628	198,566	415,542	93,393	137,254	191,293	108,817	707,975	128693	1811544	684743	56904	776193	273491	288790	837792	79837	303137	66219	469392	2,057,218	219,807	63,317	548,439	461,401	94,960	370,755	51,353
62	Population per square mile, 2010	NaN	94.4	1.2	56.3	56	239.1	48.5	738.1	460.8	350.6	168.4	211.8	19	231.1	181	54.5	34.9	109.9	104.9	43.1	594.8	839.4	174.8	66.6	63.2	87.1	6.8	23.8	24.6	147	1,195.50	17	411.2	196.1	9.7	282.3	54.7	39.9	283.9	1018.1	153.9	10.7	153.9	96.3	33.6	67.9	202.6	101.2	77.1	105	5.8
63	Land area in square miles, 2010	NaN	50,645.33	570,640.95	113,594.08	52,035.48	155,779.22	103,641.89	4,842.36	1,948.54	53,624.76	57,513.49	6,422.63	82,643.12	55,518.93	35,826.11	55,857.13	81,758.72	39,486.34	43,203.90	30,842.92	9,707.24	7,800.06	56,538.90	79,626.74	46,923.27	68,741.52	145,545.80	76,824.17	109,781.18	8,952.65	7,354.22	121298.15	47126.4	48617.91	69000.8	40860.69	68594.92	95988.01	44742.7	1033.81	30060.7	75811	41234.9	261,231.71	82,169.62	9,216.66	39,490.09	66,455.52	24,038.21	54,157.80	97,093.14
64	FIPS Code	NaN	"01"	"02"	"04"	"05"	"06"	"08"	"09"	"10"	"12"	"13"	"15"	"16"	"17"	"18"	"19"	"20"	"21"	"22"	"23"	"24"	"25"	"26"	"27"	"28"	"29"	"30"	"31"	"32"	"33"	"34"	"35"	"36"	"37"	"38"	"39"	"40"	"41"	"42"	"44"	"45"	"46"	"47"	"48"	"49"	"50"	"51"	"53"	"54"	"55"	"56"
65	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
66	NOTE: FIPS Code values are enclosed in quotes ...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
67	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
68	Value Notes	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
69	1	Includes data not distributed by county.	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
70	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
71	Fact Notes	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
72	(a)	Includes persons reporting only one race	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
73	(b)	Hispanics may be of any race, so also are incl...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
74	(c)	Economic Census - Puerto Rico data are not com...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
75	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
76	Value Flags	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
77	-	Either no or too few sample observations were ...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
78	D	Suppressed to avoid disclosure of confidential...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
79	F	Fewer than 25 firms	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
80	FN	Footnote on this item in place of data	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
81	NaN	Not available	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
82	S	Suppressed; does not meet publication standards	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
83	X	Not applicable	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
84	Z	Value greater than zero but less than half uni...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	year	month	state	permit	permit_recheck	handgun	long_gun	other	multiple	admin	prepawn_handgun	prepawn_long_gun	prepawn_other	redemption_handgun	redemption_long_gun	redemption_other	returned_handgun	returned_long_gun	returned_other	rentals_handgun	rentals_long_gun	private_sale_handgun	private_sale_long_gun	private_sale_other	return_to_seller_handgun	return_to_seller_long_gun	return_to_seller_other	totals
1854	2014	12	Oregon	67.000000	nan	14346.000000	13994.000000	0.000000	0	4.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	nan	nan	nan	nan	nan	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	28411
10146	2002	05	Mississippi	0.000000	nan	2620.000000	2725.000000	nan	157	0.000000	47.000000	98.000000	nan	898.000000	1320.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	7865
12005	1999	07	Indiana	0.000000	nan	2205.000000	10381.000000	nan	8	0.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	12594
3451	2012	07	Puerto Rico	0.000000	nan	962.000000	208.000000	3.000000	29	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	1202
10267	2002	03	Ohio	0.000000	nan	9328.000000	13407.000000	nan	791	0.000000	5.000000	37.000000	nan	503.000000	1030.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	25101
9926	2002	09	Mississippi	0.000000	nan	2555.000000	5969.000000	nan	163	0.000000	51.000000	81.000000	nan	873.000000	1667.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	11359
12025	1999	07	North Carolina	7470.000000	nan	1494.000000	9859.000000	nan	94	0.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	18917
449	2017	01	Florida	21675.000000	0.000000	51599.000000	21867.000000	3129.000000	1816	2.000000	7.000000	4.000000	0.000000	2978.000000	1210.000000	6.000000	238.000000	37.000000	3.000000	0.000000	0.000000	5.000000	0.000000	0.000000	0.000000	0.000000	0.000000	104576
274	2017	05	Wyoming	435.000000	19.000000	1589.000000	1578.000000	101.000000	91	5.000000	2.000000	0.000000	0.000000	132.000000	163.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	4.000000	7.000000	1.000000	0.000000	0.000000	0.000000	4128
5850	2008	11	Maine	0.000000	nan	1822.000000	4237.000000	nan	142	0.000000	5.000000	32.000000	nan	39.000000	90.000000	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	6367

	year	month	state	permit	permit_recheck	handgun	long_gun	other	multiple	admin	prepawn_handgun	prepawn_long_gun	redemption_handgun	redemption_long_gun	redemption_other	returned_handgun	returned_long_gun	private_sale_handgun	private_sale_long_gun	return_to_seller_long_gun	return_to_seller_other	totals	recalculation
718	2016	08	Arkansas	5990.000000	840.000000	5666.000000	6108.000000	207.000000	333	0.000000	5.000000	8.000000	1005.000000	1522.000000	1.000000	0.000000	0.000000	2.000000	3.000000	1.000000	1.000000	21692	21692.000000
7201	2006	11	Washington	4956.000000	0.000000	5817.000000	8532.000000	0.000000	236	18.000000	1.000000	4.000000	596.000000	835.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	20995	20995.000000
930	2016	05	Virginia	943.000000	0.000000	18183.000000	10896.000000	758.000000	0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.000000	2.000000	0.000000	0.000000	0.000000	0.000000	30787	30787.000000
2632	2013	10	Utah	10647.000000	0.000000	2305.000000	3902.000000	72.000000	116	0.000000	5.000000	0.000000	269.000000	495.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	17812	17812.000000
12029	1999	07	Oregon	0.000000	0.000000	4598.000000	7134.000000	0.000000	159	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	11891	11891.000000
12130	1999	05	Nevada	0.000000	0.000000	2491.000000	2021.000000	0.000000	155	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	4667	4667.000000
11128	2000	11	Kentucky	0.000000	0.000000	6552.000000	18609.000000	0.000000	378	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	25539	25539.000000
9170	2003	11	Pennsylvania	279.000000	0.000000	0.000000	54412.000000	0.000000	581	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	55272	55272.000000
12175	1999	04	Maine	0.000000	0.000000	1066.000000	1645.000000	0.000000	54	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2765	2765.000000
10819	2001	05	Oregon	22.000000	0.000000	3282.000000	5542.000000	0.000000	187	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	9033	9033.000000

	Population estimates, July 1, 2016, (V2016)	Population estimates base, April 1, 2010, (V2016)	Population, percent change - April 1, 2010 (estimates base) to July 1, 2016, (V2016)	Persons under 5 years, percent, July 1, 2016, (V2016)	Persons under 18 years, percent, July 1, 2016, (V2016)	Persons 65 years and over, percent, July 1, 2016, (V2016)	Female persons, percent, July 1, 2016, (V2016)	White alone, percent, July 1, 2016, (V2016)	Black or African American alone, percent, July 1, 2016, (V2016)	American Indian and Alaska Native alone, percent, July 1, 2016, (V2016)	Asian alone, percent, July 1, 2016, (V2016)	Native Hawaiian and Other Pacific Islander alone, percent, July 1, 2016, (V2016)	Two or More Races, percent, July 1, 2016, (V2016)	Hispanic or Latino, percent, July 1, 2016, (V2016)	White alone, not Hispanic or Latino, percent, July 1, 2016, (V2016)	Housing units, July 1, 2016, (V2016)
Florida	20612439	18804592	9.60	5.50	20.10	19.90	51.10	77.60	16.80	0.50	2.90	0.10	2.10	24.90	54.90	9301642
Missouri	6093000	5988928	1.70	6.10	22.80	16.10	50.90	83.20	11.80	0.60	2.00	0.10	2.20	4.10	79.70	2760084
Maryland	6016447	5773786	4.20	6.10	22.40	14.60	51.60	59.30	30.70	0.60	6.60	0.10	2.80	9.80	51.50	2447127
Idaho	1683140	1567650	7.40	6.80	26.00	15.10	49.90	93.30	0.80	1.80	1.50	0.20	2.40	12.30	82.40	700825
Indiana	6633053	6484136	2.30	6.40	23.80	14.90	50.70	85.60	9.70	0.40	2.20	0.10	2.00	6.80	79.60	2854546
Vermont	624594	625741	-0.20	4.90	19.00	18.10	50.60	94.60	1.30	0.40	1.80	Z	1.90	1.90	93.10	329525
Connecticut	3576452	3574114	0.10	5.20	21.10	16.10	51.20	80.60	11.80	0.50	4.70	0.10	2.30	15.70	67.70	1499116
California	39250017	37254522	5.40	6.30	23.20	13.60	50.30	72.70	6.50	1.70	14.80	0.50	3.80	38.90	37.70	14060525
Arizona	6931071	6392301	8.40	6.30	23.50	16.90	50.30	83.30	4.90	5.40	3.40	0.30	2.80	30.90	55.50	2961003
Rhode Island	1056426	1052940	0.003	0.052	0.197	0.165	0.514	0.844	0.081	0.01	0.036	0.002	0.027	0.149	0.733	462589

	Population estimates, July 1, 2016, (V2016)	Population estimates base, April 1, 2010, (V2016)	Population, percent change - April 1, 2010 (estimates base) to July 1, 2016, (V2016)	Persons under 5 years, percent, July 1, 2016, (V2016)	Persons under 18 years, percent, July 1, 2016, (V2016)	Persons 65 years and over, percent, July 1, 2016, (V2016)	Female persons, percent, July 1, 2016, (V2016)	White alone, percent, July 1, 2016, (V2016)	Black or African American alone, percent, July 1, 2016, (V2016)	American Indian and Alaska Native alone, percent, July 1, 2016, (V2016)	Asian alone, percent, July 1, 2016, (V2016)	Native Hawaiian and Other Pacific Islander alone, percent, July 1, 2016, (V2016)	Two or More Races, percent, July 1, 2016, (V2016)	Hispanic or Latino, percent, July 1, 2016, (V2016)	White alone, not Hispanic or Latino, percent, July 1, 2016, (V2016)	Housing units, July 1, 2016, (V2016)
Alabama	4863300	4780131	1.70	6.00	22.60	16.10	51.60	69.30	26.80	0.70	1.40	0.10	1.60	4.20	65.80	2230185
Alaska	741894	710249	4.50	7.30	25.20	10.40	47.70	66.10	3.80	15.20	6.30	1.30	7.30	7.00	61.20	310658
Arizona	6931071	6392301	8.40	6.30	23.50	16.90	50.30	83.30	4.90	5.40	3.40	0.30	2.80	30.90	55.50	2961003
Arkansas	2988248	2916025	2.50	6.40	23.60	16.30	50.90	79.40	15.70	1.00	1.60	0.30	2.00	7.30	72.90	1354762
California	39250017	37254522	5.40	6.30	23.20	13.60	50.30	72.70	6.50	1.70	14.80	0.50	3.80	38.90	37.70	14060525
Colorado	5540545	5029324	10.20	6.10	22.80	13.40	49.70	87.50	4.50	1.60	3.30	0.20	3.00	21.30	68.60	2339118
Connecticut	3576452	3574114	0.10	5.20	21.10	16.10	51.20	80.60	11.80	0.50	4.70	0.10	2.30	15.70	67.70	1499116
Delaware	952065	897936	6.00	5.80	21.50	17.50	51.60	70.10	22.60	0.60	4.00	0.10	2.60	9.20	62.90	426149
Florida	20612439	18804592	9.60	5.50	20.10	19.90	51.10	77.60	16.80	0.50	2.90	0.10	2.10	24.90	54.90	9301642
Georgia	10310371	9688680	6.40	6.40	24.40	13.10	51.30	61.20	32.00	0.50	4.10	0.10	2.10	9.40	53.40	4218776
Hawaii	1428557	1360301	5.00	6.40	21.60	17.10	49.80	25.80	2.20	0.40	37.70	10.20	23.70	10.40	22.10	537114
Idaho	1683140	1567650	7.40	6.80	26.00	15.10	49.90	93.30	0.80	1.80	1.50	0.20	2.40	12.30	82.40	700825
Illinois	12801539	12831574	-0.20	6.00	22.90	14.60	50.90	77.20	14.70	0.60	5.50	0.10	1.90	17.00	61.70	5326970
Indiana	6633053	6484136	2.30	6.40	23.80	14.90	50.70	85.60	9.70	0.40	2.20	0.10	2.00	6.80	79.60	2854546
Iowa	3134693	3046869	2.90	6.40	23.30	16.40	50.30	91.40	3.70	0.50	2.50	0.10	1.80	5.80	86.20	1380162
Kansas	2907289	2853129	1.90	6.70	24.60	15.00	50.20	86.60	6.20	1.20	3.00	0.10	2.90	11.60	76.30	1259864
Kentucky	4436974	4339344	2.20	6.20	22.80	15.60	50.70	88.00	8.30	0.30	1.50	0.10	1.90	3.50	85.00	1965556
Louisiana	4681666	4533479	3.30	6.60	23.80	14.40	51.10	63.20	32.60	0.80	1.80	0.10	1.60	5.00	59.00	2036975
Maine	1331479	1328364	0.20	4.90	19.10	19.40	51.00	94.80	1.50	0.70	1.20	0	1.70	1.60	93.50	730705
Maryland	6016447	5773786	4.20	6.10	22.40	14.60	51.60	59.30	30.70	0.60	6.60	0.10	2.80	9.80	51.50	2447127
Massachusetts	6811779	6547813	4.00	5.30	20.20	15.80	51.50	81.80	8.60	0.50	6.70	0.10	2.30	11.50	73.00	2858026
Michigan	9928300	9884129	0.40	5.80	22.10	16.20	50.80	79.60	14.20	0.70	3.10	0	2.40	5.00	75.40	4560055
Minnesota	5519952	5303924	4.10	6.40	23.30	15.10	50.20	85.00	6.20	1.30	4.90	0.10	2.40	5.20	80.60	2409935
Mississippi	2988726	2968103	0.70	6.30	24.10	15.10	51.50	59.30	37.70	0.60	1.10	0.10	1.20	3.10	56.90	1307441
Missouri	6093000	5988928	1.70	6.10	22.80	16.10	50.90	83.20	11.80	0.60	2.00	0.10	2.20	4.10	79.70	2760084
Montana	1042520	989414	5.40	6.00	21.80	17.70	49.70	89.20	0.60	6.60	0.80	0.10	2.70	3.60	86.50	497756
Nebraska	1907116	1826334	4.40	7.00	24.80	15.00	50.20	88.90	5.00	1.40	2.50	0.10	2.10	10.70	79.60	827156
Nevada	2940058	2700691	8.90	6.30	23.00	15.00	49.90	75.10	9.60	1.60	8.70	0.80	4.20	28.50	49.90	1221698
New Hampshire	1334795	1316461	1.40	4.80	19.50	17.00	50.50	93.80	1.50	0.30	2.70	0.10	1.70	3.50	90.80	625307
New Jersey	8944469	8791953	1.70	5.80	22.20	15.30	51.20	72.40	15.00	0.60	9.80	0.10	2.20	20.00	55.80	3604409
New Mexico	2081015	2059198	0.011	0.062	0.236	0.165	0.505	0.826	0.025	0.106	0.017	0.002	0.025	0.485	0.381	917568
New York	19745289	19378110	0.019	0.059	0.212	0.154	0.514	0.699	0.177	0.01	0.089	0.001	0.025	0.19	0.558	8231687
North Carolina	10146788	9535688	0.064	0.06	0.227	0.155	0.514	0.71	0.222	0.016	0.029	0.001	0.022	0.092	0.635	4540498
North Dakota	757952	672591	0.127	0.073	0.233	0.145	0.487	0.879	0.029	0.055	0.015	0.001	0.021	0.036	0.85	368624
Ohio	11614373	11536727	0.007	0.06	0.225	0.162	0.51	0.825	0.128	0.003	0.022	0.001	0.022	0.037	0.795	5164361
Oklahoma	3923561	3751615	0.046	0.068	0.245	0.15	0.505	0.746	0.078	0.092	0.022	0.002	0.061	0.103	0.662	1721045
Oregon	4093465	3831072	0.068	0.058	0.212	0.168	0.505	0.874	0.021	0.018	0.045	0.004	0.038	0.128	0.764	1732786
Pennsylvania	12784227	12702857	0.006	0.056	0.209	0.174	0.51	0.824	0.118	0.004	0.035	0.001	0.019	0.07	0.77	5612002
Rhode Island	1056426	1052940	0.003	0.052	0.197	0.165	0.514	0.844	0.081	0.01	0.036	0.002	0.027	0.149	0.733	462589
South Carolina	4961119	4625410	0.073	0.059	0.221	0.167	0.515	0.685	0.275	0.005	0.016	0.001	0.018	0.055	0.639	2236153
South Dakota	865454	814195	0.063	0.071	0.246	0.16	0.496	0.852	0.02	0.09	0.015	0.001	0.023	0.037	0.825	383838
Tennessee	6651194	6346298	0.048	0.061	0.226	0.157	0.512	0.787	0.171	0.004	0.018	0.001	0.019	0.052	0.742	2919671
Texas	27862596	25146100	10.80	7.20	26.20	12.00	50.40	79.40	12.60	1.00	4.80	0.10	1.90	39.10	42.60	10753629
Utah	3051217	2763888	10.40	8.30	30.20	10.50	49.70	91.10	1.40	1.60	2.50	1.00	2.50	13.80	78.80	1054164
Vermont	624594	625741	-0.20	4.90	19.00	18.10	50.60	94.60	1.30	0.40	1.80	0	1.90	1.90	93.10	329525
Virginia	8411808	8001041	5.10	6.10	22.20	14.60	50.80	70.00	19.80	0.50	6.60	0.10	2.90	9.10	62.40	3491054
Washington	7288000	6724545	8.40	6.20	22.40	14.80	50.00	80.00	4.10	1.90	8.60	0.80	4.60	12.40	69.50	3025685
West Virginia	1831102	1853011	-1.20	5.50	20.50	18.80	50.50	93.60	3.60	0.20	0.80	0	1.70	1.50	92.30	886640
Wisconsin	5778708	5687289	1.60	5.80	22.30	16.10	50.30	87.50	6.60	1.10	2.80	0.10	1.90	6.70	81.70	2668444
Wyoming	585501	563767	3.90	6.50	23.70	15.00	48.90	92.80	1.30	2.70	1.00	0.10	2.10	10.00	84.10	270600

	Population estimates, July 1, 2016, (V2016)	Persons under 5 years, percent, July 1, 2016, (V2016)	Persons under 18 years, percent, July 1, 2016, (V2016)	Persons 65 years and over, percent, July 1, 2016, (V2016)	Female persons, percent, July 1, 2016, (V2016)	White alone, percent, July 1, 2016, (V2016)	Black or African American alone, percent, July 1, 2016, (V2016)	American Indian and Alaska Native alone, percent, July 1, 2016, (V2016)	Asian alone, percent, July 1, 2016, (V2016)	Native Hawaiian and Other Pacific Islander alone, percent, July 1, 2016, (V2016)	Two or More Races, percent, July 1, 2016, (V2016)	Hispanic or Latino, percent, July 1, 2016, (V2016)	White alone, not Hispanic or Latino, percent, July 1, 2016, (V2016)	Housing units, July 1, 2016, (V2016)
Alabama	4863300	0.060000	0.226000	0.161000	0.516000	0.693000	0.268000	0.007000	0.014000	0.001000	0.016000	0.042000	0.658000	2230185
Alaska	741894	0.073000	0.252000	0.104000	0.477000	0.661000	0.038000	0.152000	0.063000	0.013000	0.073000	0.070000	0.612000	310658
Arizona	6931071	0.063000	0.235000	0.169000	0.503000	0.833000	0.049000	0.054000	0.034000	0.003000	0.028000	0.309000	0.555000	2961003
Arkansas	2988248	0.064000	0.236000	0.163000	0.509000	0.794000	0.157000	0.010000	0.016000	0.003000	0.020000	0.073000	0.729000	1354762
California	39250017	0.063000	0.232000	0.136000	0.503000	0.727000	0.065000	0.017000	0.148000	0.005000	0.038000	0.389000	0.377000	14060525

	Population estimates, July 1, 2016, (V2016)	Persons under 5 years, percent, July 1, 2016, (V2016)	Persons under 18 years, percent, July 1, 2016, (V2016)	Persons 65 years and over, percent, July 1, 2016, (V2016)	Female persons, percent, July 1, 2016, (V2016)	White alone, percent, July 1, 2016, (V2016)	Black or African American alone, percent, July 1, 2016, (V2016)	American Indian and Alaska Native alone, percent, July 1, 2016, (V2016)	Asian alone, percent, July 1, 2016, (V2016)	Native Hawaiian and Other Pacific Islander alone, percent, July 1, 2016, (V2016)	Two or More Races, percent, July 1, 2016, (V2016)	Hispanic or Latino, percent, July 1, 2016, (V2016)	White alone, not Hispanic or Latino, percent, July 1, 2016, (V2016)	Housing units, July 1, 2016, (V2016)
Maryland	6016447	0.061000	0.224000	0.146000	0.516000	0.593000	0.307000	0.006000	0.066000	0.001000	0.028000	0.098000	0.515000	2447127
Arizona	6931071	0.063000	0.235000	0.169000	0.503000	0.833000	0.049000	0.054000	0.034000	0.003000	0.028000	0.309000	0.555000	2961003
Colorado	5540545	0.061000	0.228000	0.134000	0.497000	0.875000	0.045000	0.016000	0.033000	0.002000	0.030000	0.213000	0.686000	2339118
Washington	7288000	0.062000	0.224000	0.148000	0.500000	0.800000	0.041000	0.019000	0.086000	0.008000	0.046000	0.124000	0.695000	3025685
Massachusetts	6811779	0.053000	0.202000	0.158000	0.515000	0.818000	0.086000	0.005000	0.067000	0.001000	0.023000	0.115000	0.730000	2858026
Florida	20612439	0.055000	0.201000	0.199000	0.511000	0.776000	0.168000	0.005000	0.029000	0.001000	0.021000	0.249000	0.549000	9301642
Texas	27862596	0.072000	0.262000	0.120000	0.504000	0.794000	0.126000	0.010000	0.048000	0.001000	0.019000	0.391000	0.426000	10753629
Rhode Island	1056426	0.000520	0.001970	0.001650	0.005140	0.008440	0.000810	0.000100	0.000360	0.000020	0.000270	0.001490	0.007330	462589
New Hampshire	1334795	0.048000	0.195000	0.170000	0.505000	0.938000	0.015000	0.003000	0.027000	0.001000	0.017000	0.035000	0.908000	625307
New Jersey	8944469	0.058000	0.222000	0.153000	0.512000	0.724000	0.150000	0.006000	0.098000	0.001000	0.022000	0.200000	0.558000	3604409

	permit	permit_recheck	handgun	long_gun	other	multiple	admin	prepawn_handgun	prepawn_long_gun	prepawn_other	redemption_handgun	redemption_long_gun	redemption_other	returned_handgun	returned_long_gun	returned_other	rentals_handgun	rentals_long_gun	private_sale_handgun	private_sale_long_gun	private_sale_other	return_to_seller_handgun	return_to_seller_long_gun	return_to_seller_other	totals	Population_estimates_July_1_2016__(V2016)	Persons_under_5_years_percent_July_1_2016__(V2016)	Persons_under_18_years_percent_July_1_2016__(V2016)	Persons_65_years_and_over_percent__July_1_2016__(V2016)	Female_persons_percent__July_1_2016__(V2016)	White_alone_percent_July_1_2016__(V2016)	Black_or_African_American_alone_percent_July_1_2016__(V2016)	American_Indian_and_Alaska_Native_alone_percent_July_1_2016__(V2016)	Asian_alone_percent_July_1_2016__(V2016)	Native_Hawaiian_and_Other_Pacific_Islander_alone_percent_July_1_2016__(V2016)	Two_or_More_Races_percent_July_1_2016__(V2016)	Hispanic_or_Latino_percent_July_1_2016__(V2016)	White_alone_not_Hispanic_or_Latino_percent_July_1_2016__(V2016)	Housing_units__July_1_2016__(V2016)
count	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000	50.000000
mean	19011.440000	516.120000	12550.300000	8801.020000	1065.740000	427.800000	4.600000	4.340000	3.880000	0.180000	673.700000	555.100000	3.580000	38.020000	8.580000	1.300000	0.000000	0.000000	23.840000	17.720000	1.860000	0.500000	0.720000	0.060000	43710.400000	6448926.860000	0.046968	0.174258	0.118104	0.385117	0.603970	0.083609	0.011803	0.036312	0.003604	0.023104	0.088747	0.527791	2707684.160000
std	51558.172316	2138.884247	13515.737678	8693.329327	2520.780976	556.719216	20.695065	8.230382	5.691526	0.481918	1113.581504	754.211529	6.047735	83.619764	20.412171	5.603388	0.000000	0.000000	76.968947	62.758052	4.915656	1.092647	1.641801	0.239898	61544.999748	7271769.108250	0.026973	0.099361	0.068474	0.215845	0.358619	0.099179	0.023631	0.057911	0.014447	0.033892	0.098057	0.328728	2813243.923687
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1565.000000	585501.000000	0.000520	0.001970	0.001450	0.004870	0.006850	0.000200	0.000030	0.000150	0.000000	0.000180	0.000360	0.003810	270600.000000
25%	1308.750000	0.000000	3392.250000	3390.000000	150.250000	58.750000	0.000000	0.000000	0.000000	0.000000	2.250000	8.500000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.250000	0.000000	0.000000	0.000000	0.000000	11823.000000	1850105.500000	0.048250	0.190250	0.104250	0.480000	0.341750	0.006500	0.002250	0.008000	0.000020	0.013000	0.015250	0.260000	842027.000000
50%	3810.000000	0.000000	9560.500000	6002.500000	326.500000	227.000000	0.000000	0.500000	1.000000	0.000000	178.500000	267.500000	1.000000	5.000000	1.000000	0.000000	0.000000	0.000000	3.000000	3.500000	0.000000	0.000000	0.000000	0.000000	25379.500000	4559320.000000	0.060000	0.222500	0.150000	0.503000	0.774000	0.047000	0.006000	0.023500	0.001000	0.020000	0.062500	0.620500	2001265.500000
75%	14417.750000	1.500000	15574.500000	11859.000000	1114.250000	607.500000	2.000000	6.000000	6.500000	0.000000	1158.750000	901.000000	4.750000	30.000000	6.500000	0.750000	0.000000	0.000000	11.000000	8.000000	1.000000	0.750000	1.000000	0.000000	46008.500000	7198767.750000	0.064000	0.234500	0.161000	0.509000	0.872750	0.124000	0.011750	0.045500	0.001000	0.024750	0.115750	0.796000	3009514.500000
max	342858.000000	11933.000000	57902.000000	47594.000000	17297.000000	2698.000000	145.000000	50.000000	26.000000	2.000000	6571.000000	4219.000000	33.000000	421.000000	99.000000	39.000000	0.000000	0.000000	486.000000	424.000000	31.000000	5.000000	10.000000	1.000000	363085.000000	39250017.000000	0.083000	0.302000	0.199000	0.516000	0.948000	0.377000	0.152000	0.377000	0.102000	0.237000	0.391000	0.935000	14060525.000000

	permit_per_capita	permit_recheck_per_capita	handgun_per_capita	long_gun_per_capita	other_per_capita	multiple_per_capita	admin_per_capita	prepawn_handgun_per_capita	prepawn_long_gun_per_capita	prepawn_other_per_capita	redemption_handgun_per_capita	redemption_long_gun_per_capita	redemption_other_per_capita	returned_handgun_per_capita	returned_long_gun_per_capita	returned_other_per_capita	rentals_handgun_per_capita	rentals_long_gun_per_capita	private_sale_handgun_per_capita	private_sale_long_gun_per_capita	private_sale_other_per_capita	return_to_seller_handgun_per_capita	return_to_seller_long_gun_per_capita	return_to_seller_other_per_capita	totals_per_capita
state
Oregon	0.000005	0.000000	0.003489	0.002567	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.006062
Mississippi	0.000640	0.000000	0.003488	0.002049	0.000146	0.000154	0.000001	0.000007	0.000009	0.000000	0.000472	0.000360	0.000001	0.000000	0.000000	0.000000	0.000000	0.000000	0.000002	0.000000	0.000000	0.000000	0.000000	0.000000	0.007330
Colorado	0.001150	0.000000	0.003387	0.002739	0.000272	0.000190	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000034	0.000006	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.007778
Kentucky	0.077273	0.000000	0.002114	0.001491	0.000072	0.000120	0.000000	0.000002	0.000004	0.000000	0.000394	0.000350	0.000005	0.000001	0.000001	0.000000	0.000000	0.000000	0.000003	0.000002	0.000000	0.000000	0.000000	0.000000	0.081832
New Mexico	0.000530	0.000000	0.002585	0.001906	0.000228	0.000144	0.000000	0.000001	0.000001	0.000000	0.000232	0.000222	0.000000	0.000017	0.000000	0.000000	0.000000	0.000000	0.000002	0.000002	0.000000	0.000000	0.000000	0.000000	0.005872
South Dakota	0.001362	0.000000	0.002877	0.003471	0.000320	0.000156	0.000008	0.000000	0.000001	0.000000	0.000143	0.000211	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000003	0.000003	0.000000	0.000000	0.000000	0.000000	0.008557
Wyoming	0.001020	0.000000	0.002765	0.003021	0.000224	0.000169	0.000003	0.000000	0.000009	0.000000	0.000268	0.000350	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000002	0.000000	0.000000	0.000000	0.000000	0.000000	0.007831
Arizona	0.001134	0.000044	0.001976	0.001247	0.000185	0.000093	0.000000	0.000001	0.000001	0.000000	0.000184	0.000091	0.000001	0.000015	0.000002	0.000000	0.000000	0.000000	0.000002	0.000001	0.000000	0.000000	0.000000	0.000000	0.004977
South Carolina	0.002827	0.000000	0.002009	0.001185	0.000143	0.000073	0.000001	0.000002	0.000001	0.000000	0.000214	0.000131	0.000001	0.000009	0.000000	0.000000	0.000000	0.000000	0.000002	0.000000	0.000000	0.000000	0.000000	0.000000	0.006597
Maryland	0.000396	0.000000	0.000660	0.000915	0.000017	0.000006	0.000000	0.000000	0.000000	0.000000	0.000011	0.000019	0.000000	0.000005	0.000001	0.000000	0.000000	0.000000	0.000000	0.000001	0.000000	0.000000	0.000000	0.000000	0.002032

	totals_per_capita	Population_estimates_July_1_2016__(V2016)	Persons_under_5_years_percent_July_1_2016__(V2016)	Persons_under_18_years_percent_July_1_2016__(V2016)	Persons_65_years_and_over_percent__July_1_2016__(V2016)	Female_persons_percent__July_1_2016__(V2016)	White_alone_percent_July_1_2016__(V2016)	Black_or_African_American_alone_percent_July_1_2016__(V2016)	American_Indian_and_Alaska_Native_alone_percent_July_1_2016__(V2016)	Asian_alone_percent_July_1_2016__(V2016)	Native_Hawaiian_and_Other_Pacific_Islander_alone_percent_July_1_2016__(V2016)	Two_or_More_Races_percent_July_1_2016__(V2016)	Hispanic_or_Latino_percent_July_1_2016__(V2016)	White_alone_not_Hispanic_or_Latino_percent_July_1_2016__(V2016)	Housing_units__July_1_2016__(V2016)
totals_per_capita	1.000000	-0.077510	0.095411	0.096720	0.094507	0.098138	0.148380	0.004393	-0.006352	-0.134744	-0.088835	-0.072981	-0.116628	0.190200	-0.073967
White_alone_not_Hispanic_or_Latino_percent_July_1_2016__(V2016)	0.190200	-0.215365	0.852659	0.870277	0.903047	0.897689	0.970117	0.259512	0.227864	0.035792	-0.092960	0.128806	0.220378	1.000000	-0.226173
White_alone_percent_July_1_2016__(V2016)	0.148380	-0.048748	0.909298	0.925414	0.925866	0.941178	1.000000	0.282775	0.249090	0.109817	-0.084017	0.154416	0.449167	0.970117	-0.066818
Female_persons_percent__July_1_2016__(V2016)	0.098138	-0.003959	0.971619	0.980058	0.968606	1.000000	0.941178	0.490562	0.253163	0.348469	0.132835	0.372522	0.503715	0.897689	-0.024923
Persons_under_18_years_percent_July_1_2016__(V2016)	0.096720	0.007132	0.997574	1.000000	0.911136	0.980058	0.925414	0.471225	0.314869	0.331602	0.136139	0.369513	0.537821	0.870277	-0.019465
Persons_under_5_years_percent_July_1_2016__(V2016)	0.095411	0.007799	1.000000	0.997574	0.898640	0.971619	0.909298	0.459727	0.341104	0.358306	0.170965	0.403281	0.540219	0.852659	-0.019627
Persons_65_years_and_over_percent__July_1_2016__(V2016)	0.094507	-0.059407	0.898640	0.911136	1.000000	0.968606	0.925866	0.431276	0.176847	0.328038	0.142850	0.367053	0.423451	0.903047	-0.069613
Black_or_African_American_alone_percent_July_1_2016__(V2016)	0.004393	0.114231	0.459727	0.471225	0.431276	0.490562	0.282775	1.000000	-0.093982	0.075118	-0.094227	0.008792	0.189608	0.259512	0.122468
American_Indian_and_Alaska_Native_alone_percent_July_1_2016__(V2016)	-0.006352	-0.118547	0.341104	0.314869	0.176847	0.253163	0.249090	-0.093982	1.000000	0.078819	0.076906	0.244704	0.177128	0.227864	-0.138515
Two_or_More_Races_percent_July_1_2016__(V2016)	-0.072981	-0.076691	0.403281	0.369513	0.367053	0.372522	0.154416	0.008792	0.244704	0.927122	0.953178	1.000000	0.237019	0.128806	-0.101068
Housing_units__July_1_2016__(V2016)	-0.073967	0.993921	-0.019627	-0.019465	-0.069613	-0.024923	-0.066818	0.122468	-0.138515	0.123061	-0.114362	-0.101068	0.539965	-0.226173	1.000000
Population_estimates_July_1_2016__(V2016)	-0.077510	1.000000	0.007799	0.007132	-0.059407	-0.003959	-0.048748	0.114231	-0.118547	0.159329	-0.096089	-0.076691	0.574800	-0.215365	0.993921
Native_Hawaiian_and_Other_Pacific_Islander_alone_percent_July_1_2016__(V2016)	-0.088835	-0.096089	0.170965	0.136139	0.142850	0.132835	-0.084017	-0.094227	0.076906	0.883278	1.000000	0.953178	0.092746	-0.092960	-0.114362
Hispanic_or_Latino_percent_July_1_2016__(V2016)	-0.116628	0.574800	0.540219	0.537821	0.423451	0.503715	0.449167	0.189608	0.177128	0.397812	0.092746	0.237019	1.000000	0.220378	0.539965
Asian_alone_percent_July_1_2016__(V2016)	-0.134744	0.159329	0.358306	0.331602	0.328038	0.348469	0.109817	0.075118	0.078819	1.000000	0.883278	0.927122	0.397812	0.035792	0.123061

	Kentucky
Population_estimates_July_1_2016__(V2016)	4436974.000000
Housing_units__July_1_2016__(V2016)	1965556.000000
White_alone_percent_July_1_2016__(V2016)	0.880000
White_alone_not_Hispanic_or_Latino_percent_July_1_2016__(V2016)	0.850000
Female_persons_percent__July_1_2016__(V2016)	0.507000
Persons_under_18_years_percent_July_1_2016__(V2016)	0.228000
Persons_65_years_and_over_percent__July_1_2016__(V2016)	0.156000
Black_or_African_American_alone_percent_July_1_2016__(V2016)	0.083000
Persons_under_5_years_percent_July_1_2016__(V2016)	0.062000
Hispanic_or_Latino_percent_July_1_2016__(V2016)	0.035000
Two_or_More_Races_percent_July_1_2016__(V2016)	0.019000
Asian_alone_percent_July_1_2016__(V2016)	0.015000
American_Indian_and_Alaska_Native_alone_percent_July_1_2016__(V2016)	0.003000
Native_Hawaiian_and_Other_Pacific_Islander_alone_percent_July_1_2016__(V2016)	0.001000

	Indiana
Population_estimates_July_1_2016__(V2016)	6633053.000000
Housing_units__July_1_2016__(V2016)	2854546.000000
White_alone_percent_July_1_2016__(V2016)	0.856000
White_alone_not_Hispanic_or_Latino_percent_July_1_2016__(V2016)	0.796000
Female_persons_percent__July_1_2016__(V2016)	0.507000
Persons_under_18_years_percent_July_1_2016__(V2016)	0.238000
Persons_65_years_and_over_percent__July_1_2016__(V2016)	0.149000
Black_or_African_American_alone_percent_July_1_2016__(V2016)	0.097000
Hispanic_or_Latino_percent_July_1_2016__(V2016)	0.068000
Persons_under_5_years_percent_July_1_2016__(V2016)	0.064000
Asian_alone_percent_July_1_2016__(V2016)	0.022000
Two_or_More_Races_percent_July_1_2016__(V2016)	0.020000
American_Indian_and_Alaska_Native_alone_percent_July_1_2016__(V2016)	0.004000
Native_Hawaiian_and_Other_Pacific_Islander_alone_percent_July_1_2016__(V2016)	0.001000

	year	month	permit	permit_recheck	handgun	long_gun	other	multiple	admin	prepawn_handgun	prepawn_long_gun	prepawn_other	redemption_handgun	redemption_long_gun	redemption_other	returned_handgun	returned_long_gun	returned_other	rentals_handgun	rentals_long_gun	private_sale_handgun	private_sale_long_gun	private_sale_other	return_to_seller_handgun	return_to_seller_long_gun	return_to_seller_other	totals
state
New York	2012	07	2166	0	6002	11448	372	86	205	0	0	0	0	5	0	0	0	0	0	0	0	0	0	0	0	0	20284
Nebraska	2003	11	1219	0	13	4235	0	3	0	0	1	0	3	155	0	0	0	0	0	0	0	0	0	0	0	0	5629
Kentucky	2002	05	0	0	4805	5534	0	345	0	53	110	0	1041	1799	0	0	0	0	0	0	0	0	0	0	0	0	13687
Georgia	1999	06	3730	0	7703	6588	0	236	6700	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	24957
New York	2014	10	3703	0	8190	21900	392	152	0	1	1	0	7	19	0	0	0	0	0	0	153	127	6	6	2	0	34659
Washington	2005	02	2098	0	5721	7401	0	339	7	6	3	0	885	1227	0	0	0	0	0	0	0	0	0	0	0	0	17687
Wyoming	1999	01	67	0	668	1401	0	44	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2180
Nebraska	2003	02	1635	0	12	1545	0	1	0	0	0	0	2	125	0	0	0	0	0	0	0	0	0	0	0	0	3320
Maryland	2009	11	0	0	3567	5674	12	9	0	0	0	0	48	188	0	0	0	0	0	0	0	0	0	0	0	0	9498
Vermont	2014	06	0	0	867	787	48	32	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	1736

	year	month	permit	permit_recheck	handgun	long_gun	other	multiple	admin	prepawn_handgun	prepawn_long_gun	prepawn_other	redemption_handgun	redemption_long_gun	redemption_other	returned_handgun	returned_long_gun	returned_other	rentals_handgun	rentals_long_gun	private_sale_handgun	private_sale_long_gun	private_sale_other	return_to_seller_handgun	return_to_seller_long_gun	return_to_seller_other	totals
state
Alabama	2017	07	18042	1	6046	4790	224	258	0	7	13	0	1110	963	0	0	0	0	0	0	12	8	0	0	0	0	31474
Alaska	2017	07	222	5	2911	2458	238	180	0	3	3	0	201	164	0	16	17	9	0	0	31	27	1	0	1	0	6487
Arizona	2017	07	5606	126	10194	6363	999	544	0	8	4	1	1230	516	1	81	16	0	0	0	13	12	0	0	0	0	25714
Arkansas	2017	07	2564	571	4343	3804	189	254	44	9	5	0	1009	1195	1	0	0	0	0	0	9	17	1	0	0	0	14015
California	2017	07	52610	0	37027	21721	2514	0	0	0	0	0	393	322	8	0	0	0	0	0	0	0	0	0	0	0	114595
Colorado	2017	07	4392	0	16063	11065	1004	1020	0	0	0	0	0	0	0	218	49	0	0	0	0	0	0	0	0	0	33811
Connecticut	2017	07	6387	274	5450	1771	280	0	362	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	14525
Delaware	2017	07	305	0	1417	1000	71	60	0	0	0	0	24	19	4	0	0	0	0	0	57	22	2	0	0	0	2981
District of Columbia	2017	07	19	0	44	5	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	68
Florida	2017	07	16978	0	47430	18941	2975	1844	20	7	2	2	3115	1143	5	302	48	3	0	0	5	2	0	0	0	0	92822
Georgia	2017	07	14623	0	10685	6166	365	400	0	17	7	0	1674	1107	7	4	0	0	0	0	18	9	1	1	1	0	35085
Guam	2017	07	0	0	71	45	15	14	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	145
Hawaii	2017	07	1107	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	1109
Idaho	2017	07	4539	0	2887	3276	191	149	0	2	3	0	319	431	0	34	14	0	0	0	8	14	2	0	2	0	11871
Illinois	2017	07	13652	65271	18417	9895	0	765	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	108000
Indiana	2017	07	19386	0	15385	9995	950	584	194	1	7	0	96	422	11	20	0	0	0	0	48	39	0	3	4	0	47145
Iowa	2017	07	5099	1846	94	1649	22	4	12	0	0	0	0	49	1	18	3	0	0	0	0	4	0	0	0	0	8801
Kansas	2017	07	3667	3	4636	3914	325	278	0	2	1	1	580	319	6	22	3	2	0	0	4	5	5	0	1	1	13775
Kentucky	2017	07	374603	0	7236	5118	241	419	0	9	7	0	1548	1249	2	7	2	0	0	0	26	25	1	0	0	1	390494
Louisiana	2017	07	1930	0	9420	5992	663	575	0	4	4	1	1058	761	1	0	0	0	0	0	20	18	2	1	3	1	20454
Maine	2017	07	390	0	2695	2259	144	122	0	1	0	0	83	115	0	3	5	0	0	0	12	6	0	1	0	1	5837
Mariana Islands	2017	07	0	0	11	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	13
Maryland	2017	07	2025	0	3710	3922	172	18	0	0	1	1	58	121	5	24	12	0	0	0	0	18	0	1	0	0	10088
Massachusetts	2017	07	6185	0	4034	2150	332	118	3	0	0	0	1	0	0	1	0	0	0	0	38	25	11	1	1	0	12900
Michigan	2017	07	15464	14	9305	7687	339	208	0	0	1	0	5	240	0	68	13	10	0	0	18	6	0	0	0	0	33378
Minnesota	2017	07	27018	0	5181	7734	430	245	0	1	0	0	176	347	0	11	7	4	0	0	16	13	2	1	0	0	41186
Mississippi	2017	07	1506	0	5961	3980	247	320	0	24	14	0	1336	977	2	0	0	0	0	0	13	5	1	0	1	1	14388
Missouri	2017	07	716	0	16151	11098	1013	857	0	6	6	0	1344	937	4	131	19	2	0	0	66	54	4	4	2	1	32415
Montana	2017	07	1043	0	2307	3168	137	156	10	1	3	0	418	619	1	18	1	2	0	0	1	3	0	0	2	0	7890
Nebraska	2017	07	2656	221	97	1204	10	0	0	2	1	0	6	50	1	11	4	0	0	0	0	3	0	0	0	0	4266
Nevada	2017	07	1805	0	3647	1962	219	223	0	0	0	0	317	137	0	0	0	0	0	0	0	0	0	0	0	0	8310
New Hampshire	2017	07	1451	0	4228	2427	128	4	11	0	0	0	0	11	1	64	6	0	0	0	0	2	0	0	0	0	8333
New Jersey	2017	07	0	0	4351	2519	164	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	7034
New Mexico	2017	07	1009	0	4834	3254	361	228	0	3	3	0	428	400	3	43	0	0	0	0	8	5	1	0	0	0	10580
New York	2017	07	2315	1053	9212	11301	879	170	0	1	0	0	14	17	0	21	17	0	2	2	356	187	16	9	3	1	25576
North Carolina	2017	07	20102	0	1059	9316	551	180	0	8	9	0	1831	1688	8	0	0	0	3	0	7	18	1	1	2	0	34784
North Dakota	2017	07	496	0	1286	2030	85	62	4	2	0	0	70	87	0	0	0	0	0	0	4	8	2	0	0	0	4136
Ohio	2017	07	9440	852	20990	14131	1227	959	0	4	2	1	1143	605	4	12	4	0	0	0	31	22	1	2	1	1	49432
Oklahoma	2017	07	0	0	9377	6698	882	668	0	19	9	0	1626	1369	8	0	0	0	0	0	37	41	3	1	0	0	20738

	year	month	permit	permit_recheck	handgun	long_gun	other	multiple	admin	prepawn_handgun	prepawn_long_gun	prepawn_other	redemption_handgun	redemption_long_gun	redemption_other	returned_handgun	returned_long_gun	returned_other	rentals_handgun	rentals_long_gun	private_sale_handgun	private_sale_long_gun	private_sale_other	return_to_seller_handgun	return_to_seller_long_gun	return_to_seller_other	totals
state
Delaware	2016	07	755	0	1859	1569	140	74	0	0	1	0	15	8	0	0	0	0	0	0	45	22	5	1	0	0	4494
Hawaii	2016	07	1563	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1565
Wisconsin	2016	07	14505	0	13182	9870	847	38	0	0	2	1	131	260	6	24	27	4	0	0	11	8	3	0	3	0	38922
Michigan	2016	07	15869	3753	11308	8095	510	239	0	0	0	0	14	275	0	62	12	1	0	0	2	1	1	0	0	0	40142
South Carolina	2016	07	14025	0	9967	5880	707	361	5	8	4	0	1063	648	4	43	1	0	0	0	11	1	1	1	0	0	32730
North Dakota	2016	07	1305	0	1333	2457	116	64	0	0	3	0	67	118	2	0	0	0	0	0	3	2	0	0	0	0	5470
Puerto Rico	2016	07	0	0	1038	234	31	20	0	0	0	0	7	0	0	0	0	0	0	0	0	0	1	0	0	0	1331
Illinois	2016	07	125075	8969	21421	11881	0	881	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	168227
Virginia	2016	07	1102	0	24671	16731	1064	0	0	0	0	0	0	0	0	4	2	0	0	0	0	0	0	0	0	0	43574
Georgia	2016	07	20555	0	15342	8597	635	632	0	12	8	0	1999	1392	4	0	0	0	0	0	3	3	1	0	0	0	49183

	Illinois
Population_estimates_July_1_2016__(V2016)	12801539.000000
Housing_units__July_1_2016__(V2016)	5326970.000000
White_alone_percent_July_1_2016__(V2016)	0.772000
White_alone_not_Hispanic_or_Latino_percent_July_1_2016__(V2016)	0.617000
Female_persons_percent__July_1_2016__(V2016)	0.509000
Persons_under_18_years_percent_July_1_2016__(V2016)	0.229000
Hispanic_or_Latino_percent_July_1_2016__(V2016)	0.170000
Black_or_African_American_alone_percent_July_1_2016__(V2016)	0.147000
Persons_65_years_and_over_percent__July_1_2016__(V2016)	0.146000
Persons_under_5_years_percent_July_1_2016__(V2016)	0.060000
Asian_alone_percent_July_1_2016__(V2016)	0.055000
Two_or_More_Races_percent_July_1_2016__(V2016)	0.019000
American_Indian_and_Alaska_Native_alone_percent_July_1_2016__(V2016)	0.006000
Native_Hawaiian_and_Other_Pacific_Islander_alone_percent_July_1_2016__(V2016)	0.001000

	Alabama
Population_estimates_July_1_2016__(V2016)	4863300.000000
Housing_units__July_1_2016__(V2016)	2230185.000000
White_alone_percent_July_1_2016__(V2016)	0.693000
White_alone_not_Hispanic_or_Latino_percent_July_1_2016__(V2016)	0.658000
Female_persons_percent__July_1_2016__(V2016)	0.516000
Black_or_African_American_alone_percent_July_1_2016__(V2016)	0.268000
Persons_under_18_years_percent_July_1_2016__(V2016)	0.226000
Persons_65_years_and_over_percent__July_1_2016__(V2016)	0.161000
Persons_under_5_years_percent_July_1_2016__(V2016)	0.060000
Hispanic_or_Latino_percent_July_1_2016__(V2016)	0.042000
Two_or_More_Races_percent_July_1_2016__(V2016)	0.016000
Asian_alone_percent_July_1_2016__(V2016)	0.014000
American_Indian_and_Alaska_Native_alone_percent_July_1_2016__(V2016)	0.007000
Native_Hawaiian_and_Other_Pacific_Islander_alone_percent_July_1_2016__(V2016)	0.001000

	New Hampshire
Population_estimates_July_1_2016__(V2016)	1334795.000000
Housing_units__July_1_2016__(V2016)	625307.000000
White_alone_percent_July_1_2016__(V2016)	0.938000
White_alone_not_Hispanic_or_Latino_percent_July_1_2016__(V2016)	0.908000
Female_persons_percent__July_1_2016__(V2016)	0.505000
Persons_under_18_years_percent_July_1_2016__(V2016)	0.195000
Persons_65_years_and_over_percent__July_1_2016__(V2016)	0.170000
Persons_under_5_years_percent_July_1_2016__(V2016)	0.048000
Hispanic_or_Latino_percent_July_1_2016__(V2016)	0.035000
Asian_alone_percent_July_1_2016__(V2016)	0.027000
Two_or_More_Races_percent_July_1_2016__(V2016)	0.017000
Black_or_African_American_alone_percent_July_1_2016__(V2016)	0.015000
American_Indian_and_Alaska_Native_alone_percent_July_1_2016__(V2016)	0.003000
Native_Hawaiian_and_Other_Pacific_Islander_alone_percent_July_1_2016__(V2016)	0.001000